{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-10T17:34:14.044978Z",
     "start_time": "2017-05-10T17:34:11.462872Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Input, Dense, Lambda, Layer\n",
    "from keras.models import Model\n",
    "from keras import regularizers\n",
    "import keras\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from keras import backend as K\n",
    "from keras import metrics\n",
    "from collections import namedtuple\n",
    "pd.set_option(\"display.max_rows\",35)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-10T17:34:15.330907Z",
     "start_time": "2017-05-10T17:34:14.047268Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kdd_train_2labels = pd.read_pickle(\"dataset/kdd_train_2labels.pkl\")\n",
    "kdd_test_2labels = pd.read_pickle(\"dataset/kdd_test_2labels.pkl\")\n",
    "\n",
    "#y_train_labels = pd.read_pickle(\"dataset/kdd_train_2labels_y.pkl\")\n",
    "#y_train_labels = pd.read_pickle(\"dataset/kdd_train_2labels.pkl\")\n",
    "#y_test_labels = pd.read_pickle(\"dataset/kdd_test_2labels_y.pkl\")\n",
    "\n",
    "output_columns_2labels = ['is_Attack','is_Normal']\n",
    "\n",
    "from sklearn import model_selection as ms\n",
    "from sklearn import preprocessing as pp\n",
    "\n",
    "x_input = kdd_train_2labels.drop(output_columns_2labels, axis = 1)\n",
    "y_output = kdd_train_2labels.loc[:,output_columns_2labels]\n",
    "\n",
    "ss = pp.StandardScaler()\n",
    "x_input = ss.fit_transform(x_input)\n",
    "\n",
    "#le = pp.LabelEncoder()\n",
    "#y_train = le.fit_transform(y_train_labels).reshape(-1, 1)\n",
    "#y_test = le.transform(y_test_labels).reshape(-1, 1)\n",
    "\n",
    "y_train = kdd_train_2labels.loc[:,output_columns_2labels]\n",
    "\n",
    "x_train, x_valid, y_train, y_valid = ms.train_test_split(x_input, \n",
    "                              y_train, \n",
    "                              test_size=0.1)\n",
    "#x_valid, x_test, y_valid, y_test = ms.train_test_split(x_valid, y_valid, test_size = 0.4)\n",
    "\n",
    "x_test = kdd_test_2labels.drop(output_columns_2labels, axis = 1)\n",
    "y_test = kdd_test_2labels.loc[:,output_columns_2labels]\n",
    "\n",
    "x_test = ss.transform(x_test)\n",
    "\n",
    "#x_train = np.hstack((x_train, y_train))\n",
    "#x_valid = np.hstack((x_valid, y_valid))\n",
    "\n",
    "#x_test = np.hstack((x_test, np.random.normal(loc = 0, scale = 0.01, size = y_test.shape)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-10T17:34:15.423418Z",
     "start_time": "2017-05-10T17:34:15.333396Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_dim = 122\n",
    "intermediate_dim = 122\n",
    "latent_dim = 32\n",
    "batch_size = 1409\n",
    "epochs = 5\n",
    "hidden_layers = 8\n",
    "\n",
    "class Train:\n",
    "    def train():\n",
    "        Train.x = Input(shape=(input_dim,))\n",
    "        \n",
    "        hidden_encoder = Train.x\n",
    "        for i in range(hidden_layers):\n",
    "            hidden_encoder = Dense(intermediate_dim, activation='relu')(hidden_encoder)\n",
    "\n",
    "        Train.mean_encoder = Dense(latent_dim, activation=None)(hidden_encoder)\n",
    "        Train.logvar_encoder = Dense(latent_dim, activation=None)(hidden_encoder)\n",
    "\n",
    "        def get_distrib(args):\n",
    "\n",
    "            m_e, l_e = args\n",
    "\n",
    "            # Sample epsilon\n",
    "            epsilon = np.random.normal(loc=0.0, scale=0.05, size = (batch_size, latent_dim))\n",
    "\n",
    "            # Sample latent variable\n",
    "            z = m_e + K.exp(l_e / 2) * epsilon\n",
    "            return z\n",
    "\n",
    "        z = Lambda(get_distrib)([Train.mean_encoder, Train.logvar_encoder])\n",
    "\n",
    "        hidden_decoder = z\n",
    "        for i in range(hidden_layers):\n",
    "            hidden_decoder = Dense(intermediate_dim, activation=\"relu\")(hidden_decoder)\n",
    "\n",
    "        Train.x_ = Dense(input_dim, activation=None)(hidden_decoder)\n",
    "\n",
    "def vae_loss(x, x_decoded_mean):\n",
    "    xent_loss = input_dim * keras.losses.binary_crossentropy(x, x_decoded_mean)\n",
    "    kl_loss = - 0.5 * K.sum(1 + Train.logvar_encoder - K.square(Train.mean_encoder) - K.exp(Train.logvar_encoder), axis=-1)\n",
    "    return K.abs(K.mean(xent_loss + kl_loss))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-10T18:04:58.780614Z",
     "start_time": "2017-05-10T17:34:15.425754Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      " Current Layer Attributes - epochs:50 hidden layers:2 features count:2\n",
      "Train on 112720 samples, validate on 22544 samples\n",
      "Epoch 1/50\n",
      "112720/112720 [==============================] - 1s - loss: 25858278431901.1602 - acc: 0.0061 - val_loss: 7.8063 - val_acc: 0.0109\n",
      "Epoch 2/50\n",
      "112720/112720 [==============================] - 1s - loss: 3.1601 - acc: 0.0069 - val_loss: 6.9161 - val_acc: 0.0100\n",
      "Epoch 3/50\n",
      "112720/112720 [==============================] - 1s - loss: 3.2953 - acc: 0.0067 - val_loss: 6.6863 - val_acc: 0.0096\n",
      "Epoch 4/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.8461 - acc: 0.0065 - val_loss: 5.9909 - val_acc: 0.0078\n",
      "Epoch 5/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.9632 - acc: 0.0065 - val_loss: 6.0881 - val_acc: 0.0089\n",
      "Epoch 6/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.8493 - acc: 0.0065 - val_loss: 6.1279 - val_acc: 0.0094\n",
      "Epoch 7/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.3501 - acc: 0.0069 - val_loss: 5.9200 - val_acc: 0.0110\n",
      "Epoch 8/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.6987 - acc: 0.0071 - val_loss: 5.7445 - val_acc: 0.0110\n",
      "Epoch 9/50\n",
      "112720/112720 [==============================] - 1s - loss: 3.1856 - acc: 0.0069 - val_loss: 5.8930 - val_acc: 0.0106\n",
      "Epoch 10/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.6068 - acc: 0.0069 - val_loss: 5.7616 - val_acc: 0.0110\n",
      "Epoch 11/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.6290 - acc: 0.0068 - val_loss: 5.8220 - val_acc: 0.0103\n",
      "Epoch 12/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.6229 - acc: 0.0067 - val_loss: 5.7759 - val_acc: 0.0105\n",
      "Epoch 13/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.8511 - acc: 0.0070 - val_loss: 5.7629 - val_acc: 0.0105\n",
      "Epoch 14/50\n",
      "112720/112720 [==============================] - 1s - loss: 3.0773 - acc: 0.0069 - val_loss: 5.6141 - val_acc: 0.0106\n",
      "Epoch 15/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.7197 - acc: 0.0066 - val_loss: 5.5599 - val_acc: 0.0087\n",
      "Epoch 16/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.9921 - acc: 0.0064 - val_loss: 5.5772 - val_acc: 0.0085\n",
      "Epoch 17/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.7683 - acc: 0.0063 - val_loss: 5.5458 - val_acc: 0.0085\n",
      "Epoch 18/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.8664 - acc: 0.0063 - val_loss: 5.4602 - val_acc: 0.0085\n",
      "Epoch 19/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.6411 - acc: 0.0062 - val_loss: 5.3722 - val_acc: 0.0084\n",
      "Epoch 20/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.7367 - acc: 0.0062 - val_loss: 5.4179 - val_acc: 0.0081\n",
      "Epoch 21/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.6304 - acc: 0.0063 - val_loss: 5.4646 - val_acc: 0.0084\n",
      "Epoch 22/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.3782 - acc: 0.0063 - val_loss: 5.4822 - val_acc: 0.0086\n",
      "Epoch 23/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.8621 - acc: 0.0061 - val_loss: 5.5556 - val_acc: 0.0085\n",
      "Epoch 24/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.5564 - acc: 0.0064 - val_loss: 5.5589 - val_acc: 0.0083\n",
      "Epoch 25/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.9540 - acc: 0.0062 - val_loss: 5.5634 - val_acc: 0.0084\n",
      "Epoch 26/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.7976 - acc: 0.0063 - val_loss: 5.5160 - val_acc: 0.0084\n",
      "Epoch 27/50\n",
      "112720/112720 [==============================] - 1s - loss: 3.1914 - acc: 0.0063 - val_loss: 5.5512 - val_acc: 0.0084\n",
      "Epoch 28/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.4612 - acc: 0.0064 - val_loss: 5.5234 - val_acc: 0.0085\n",
      "Epoch 29/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.8834 - acc: 0.0064 - val_loss: 5.5244 - val_acc: 0.0086\n",
      "Epoch 30/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.6498 - acc: 0.0064 - val_loss: 5.5308 - val_acc: 0.0084\n",
      "Epoch 31/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.9258 - acc: 0.0062 - val_loss: 5.5197 - val_acc: 0.0083\n",
      "Epoch 32/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.7247 - acc: 0.0062 - val_loss: 5.5261 - val_acc: 0.0083\n",
      "Epoch 33/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.5625 - acc: 0.0064 - val_loss: 5.5103 - val_acc: 0.0083\n",
      "Epoch 34/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.5367 - acc: 0.0065 - val_loss: 5.5025 - val_acc: 0.0083\n",
      "Epoch 35/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.8489 - acc: 0.0065 - val_loss: 5.4842 - val_acc: 0.0085\n",
      "Epoch 36/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.8829 - acc: 0.0065 - val_loss: 5.4899 - val_acc: 0.0089\n",
      "Epoch 37/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.9520 - acc: 0.0066 - val_loss: 5.4852 - val_acc: 0.0089\n",
      "Epoch 38/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.4505 - acc: 0.0064 - val_loss: 5.4796 - val_acc: 0.0091\n",
      "Epoch 39/50\n",
      "112720/112720 [==============================] - 1s - loss: 3.0495 - acc: 0.0065 - val_loss: 5.4816 - val_acc: 0.0091\n",
      "Epoch 40/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.9779 - acc: 0.0065 - val_loss: 5.4735 - val_acc: 0.0092\n",
      "Epoch 41/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.8553 - acc: 0.0065 - val_loss: 5.4744 - val_acc: 0.0091\n",
      "Epoch 42/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.5721 - acc: 0.0065 - val_loss: 5.4594 - val_acc: 0.0090\n",
      "Epoch 43/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.7961 - acc: 0.0066 - val_loss: 5.4790 - val_acc: 0.0086\n",
      "Epoch 44/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.8090 - acc: 0.0064 - val_loss: 5.5016 - val_acc: 0.0086\n",
      "Epoch 45/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.6608 - acc: 0.0064 - val_loss: 5.4638 - val_acc: 0.0087\n",
      "Epoch 46/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.8900 - acc: 0.0064 - val_loss: 5.4843 - val_acc: 0.0089\n",
      "Epoch 47/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.9061 - acc: 0.0062 - val_loss: 5.4441 - val_acc: 0.0088\n",
      "Epoch 48/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.9698 - acc: 0.0066 - val_loss: 5.4270 - val_acc: 0.0088\n",
      "Epoch 49/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.3655 - acc: 0.0063 - val_loss: 5.4230 - val_acc: 0.0088\n",
      "Epoch 50/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.7624 - acc: 0.0064 - val_loss: 5.4617 - val_acc: 0.0087\n",
      "12681/22544 [===============>..............] - ETA: 0s\n",
      " Train Acc: 0.005500354920513928, Test Acc: 0.008694109303178266\n",
      " \n",
      " Current Layer Attributes - epochs:50 hidden layers:2 features count:4\n",
      "Train on 112720 samples, validate on 22544 samples\n",
      "Epoch 1/50\n",
      "112720/112720 [==============================] - 1s - loss: 1987782.5185 - acc: 0.0015 - val_loss: 218.1183 - val_acc: 0.0098\n",
      "Epoch 2/50\n",
      "112720/112720 [==============================] - 1s - loss: 7.2489 - acc: 5.9439e-04 - val_loss: 189.4591 - val_acc: 0.0104\n",
      "Epoch 3/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.7638 - acc: 6.2988e-04 - val_loss: 184.1054 - val_acc: 0.0091\n",
      "Epoch 4/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.8103 - acc: 7.2747e-04 - val_loss: 182.9768 - val_acc: 0.0087\n",
      "Epoch 5/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.5780 - acc: 8.3392e-04 - val_loss: 170.0315 - val_acc: 0.0091\n",
      "Epoch 6/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.3161 - acc: 0.0012 - val_loss: 167.3655 - val_acc: 0.0088\n",
      "Epoch 7/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.7394 - acc: 0.0011 - val_loss: 162.8404 - val_acc: 0.0085\n",
      "Epoch 8/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.6514 - acc: 0.0016 - val_loss: 161.2321 - val_acc: 0.0089\n",
      "Epoch 9/50\n",
      "112720/112720 [==============================] - 1s - loss: 5.7290 - acc: 0.0018 - val_loss: 156.2200 - val_acc: 0.0093\n",
      "Epoch 10/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "112720/112720 [==============================] - 1s - loss: 6.0041 - acc: 0.0018 - val_loss: 154.0604 - val_acc: 0.0090\n",
      "Epoch 11/50\n",
      "112720/112720 [==============================] - 1s - loss: 5.9748 - acc: 0.0018 - val_loss: 148.9816 - val_acc: 0.0092\n",
      "Epoch 12/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.1378 - acc: 0.0018 - val_loss: 148.2061 - val_acc: 0.0091\n",
      "Epoch 13/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.7567 - acc: 0.0019 - val_loss: 144.9416 - val_acc: 0.0092\n",
      "Epoch 14/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.0175 - acc: 0.0019 - val_loss: 143.1520 - val_acc: 0.0090\n",
      "Epoch 15/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.2678 - acc: 0.0018 - val_loss: 139.9805 - val_acc: 0.0089\n",
      "Epoch 16/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.2400 - acc: 0.0018 - val_loss: 138.2695 - val_acc: 0.0091\n",
      "Epoch 17/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.2751 - acc: 0.0017 - val_loss: 135.4481 - val_acc: 0.0091\n",
      "Epoch 18/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.2290 - acc: 0.0016 - val_loss: 134.4202 - val_acc: 0.0093\n",
      "Epoch 19/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.2199 - acc: 0.0015 - val_loss: 131.5463 - val_acc: 0.0093\n",
      "Epoch 20/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.0789 - acc: 0.0015 - val_loss: 128.8553 - val_acc: 0.0092\n",
      "Epoch 21/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.6345 - acc: 0.0015 - val_loss: 128.0360 - val_acc: 0.0091\n",
      "Epoch 22/50\n",
      "112720/112720 [==============================] - 1s - loss: 5.9160 - acc: 0.0016 - val_loss: 127.2901 - val_acc: 0.0090\n",
      "Epoch 23/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.5784 - acc: 0.0017 - val_loss: 126.1275 - val_acc: 0.0089\n",
      "Epoch 24/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.0304 - acc: 0.0019 - val_loss: 125.6660 - val_acc: 0.0088\n",
      "Epoch 25/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.1501 - acc: 0.0016 - val_loss: 127.5382 - val_acc: 0.0087\n",
      "Epoch 26/50\n",
      "112720/112720 [==============================] - 1s - loss: 5.8352 - acc: 0.0016 - val_loss: 127.0093 - val_acc: 0.0088\n",
      "Epoch 27/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.2596 - acc: 0.0016 - val_loss: 126.6959 - val_acc: 0.0088\n",
      "Epoch 28/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.0476 - acc: 0.0017 - val_loss: 126.0717 - val_acc: 0.0089\n",
      "Epoch 29/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.2305 - acc: 0.0019 - val_loss: 126.4630 - val_acc: 0.0087\n",
      "Epoch 30/50\n",
      "112720/112720 [==============================] - 0s - loss: 5.9022 - acc: 0.0019 - val_loss: 127.5075 - val_acc: 0.0087\n",
      "Epoch 31/50\n",
      "112720/112720 [==============================] - 0s - loss: 6.0304 - acc: 0.0017 - val_loss: 128.3141 - val_acc: 0.0089\n",
      "Epoch 32/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.1661 - acc: 0.0019 - val_loss: 127.2414 - val_acc: 0.0089\n",
      "Epoch 33/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.0558 - acc: 0.0018 - val_loss: 127.5828 - val_acc: 0.0089\n",
      "Epoch 34/50\n",
      "112720/112720 [==============================] - 1s - loss: 5.8521 - acc: 0.0018 - val_loss: 126.9953 - val_acc: 0.0088\n",
      "Epoch 35/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.2009 - acc: 0.0017 - val_loss: 126.9468 - val_acc: 0.0088\n",
      "Epoch 36/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.0885 - acc: 0.0020 - val_loss: 126.1000 - val_acc: 0.0087\n",
      "Epoch 37/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.2943 - acc: 0.0017 - val_loss: 125.3741 - val_acc: 0.0086\n",
      "Epoch 38/50\n",
      "112720/112720 [==============================] - 1s - loss: 5.8398 - acc: 0.0015 - val_loss: 123.6411 - val_acc: 0.0085\n",
      "Epoch 39/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.2061 - acc: 0.0014 - val_loss: 122.5552 - val_acc: 0.0085\n",
      "Epoch 40/50\n",
      "112720/112720 [==============================] - 1s - loss: 5.5103 - acc: 0.0015 - val_loss: 123.1002 - val_acc: 0.0086\n",
      "Epoch 41/50\n",
      "112720/112720 [==============================] - 1s - loss: 5.9220 - acc: 0.0015 - val_loss: 121.0318 - val_acc: 0.0083\n",
      "Epoch 42/50\n",
      "112720/112720 [==============================] - 1s - loss: 5.9217 - acc: 0.0014 - val_loss: 119.8391 - val_acc: 0.0082\n",
      "Epoch 43/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.1642 - acc: 0.0013 - val_loss: 119.3035 - val_acc: 0.0082\n",
      "Epoch 44/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.1597 - acc: 0.0013 - val_loss: 118.8436 - val_acc: 0.0083\n",
      "Epoch 45/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.1302 - acc: 0.0012 - val_loss: 118.9882 - val_acc: 0.0083\n",
      "Epoch 46/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.2740 - acc: 0.0015 - val_loss: 119.4133 - val_acc: 0.0083\n",
      "Epoch 47/50\n",
      "112720/112720 [==============================] - 1s - loss: 5.8251 - acc: 0.0015 - val_loss: 117.7344 - val_acc: 0.0083\n",
      "Epoch 48/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.1549 - acc: 0.0012 - val_loss: 117.7646 - val_acc: 0.0083\n",
      "Epoch 49/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.0245 - acc: 0.0013 - val_loss: 117.4752 - val_acc: 0.0082\n",
      "Epoch 50/50\n",
      "112720/112720 [==============================] - 1s - loss: 5.8720 - acc: 0.0012 - val_loss: 116.9956 - val_acc: 0.0082\n",
      "12681/22544 [===============>..............] - ETA: 0s\n",
      " Train Acc: 0.0013307310291565955, Test Acc: 0.008206174650695175\n",
      " \n",
      " Current Layer Attributes - epochs:50 hidden layers:2 features count:8\n",
      "Train on 112720 samples, validate on 22544 samples\n",
      "Epoch 1/50\n",
      "112720/112720 [==============================] - 2s - loss: 188108339.7104 - acc: 0.0045 - val_loss: 2420.6564 - val_acc: 0.0036\n",
      "Epoch 2/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.9826 - acc: 0.0049 - val_loss: 2328.8712 - val_acc: 0.0037\n",
      "Epoch 3/50\n",
      "112720/112720 [==============================] - 1s - loss: 6.1304 - acc: 0.0049 - val_loss: 2360.7164 - val_acc: 0.0041\n",
      "Epoch 4/50\n",
      "112720/112720 [==============================] - 1s - loss: 5.3371 - acc: 0.0050 - val_loss: 2142.7424 - val_acc: 0.0034\n",
      "Epoch 5/50\n",
      "112720/112720 [==============================] - 1s - loss: 5.5584 - acc: 0.0049 - val_loss: 2056.4037 - val_acc: 0.0029\n",
      "Epoch 6/50\n",
      "112720/112720 [==============================] - 1s - loss: 5.1853 - acc: 0.0050 - val_loss: 1971.8608 - val_acc: 0.0032\n",
      "Epoch 7/50\n",
      "112720/112720 [==============================] - 1s - loss: 5.1320 - acc: 0.0051 - val_loss: 1841.8940 - val_acc: 0.0033\n",
      "Epoch 8/50\n",
      "112720/112720 [==============================] - 1s - loss: 4.5824 - acc: 0.0050 - val_loss: 1804.2187 - val_acc: 0.0035\n",
      "Epoch 9/50\n",
      "112720/112720 [==============================] - 1s - loss: 4.7398 - acc: 0.0049 - val_loss: 1811.0429 - val_acc: 0.0028\n",
      "Epoch 10/50\n",
      "112720/112720 [==============================] - 1s - loss: 4.7919 - acc: 0.0049 - val_loss: 1761.5248 - val_acc: 0.0028\n",
      "Epoch 11/50\n",
      "112720/112720 [==============================] - 1s - loss: 4.6284 - acc: 0.0049 - val_loss: 1740.4090 - val_acc: 0.0031\n",
      "Epoch 12/50\n",
      "112720/112720 [==============================] - 1s - loss: 4.7743 - acc: 0.0049 - val_loss: 1674.8666 - val_acc: 0.0032\n",
      "Epoch 13/50\n",
      "112720/112720 [==============================] - 1s - loss: 4.8369 - acc: 0.0049 - val_loss: 1672.7766 - val_acc: 0.0033\n",
      "Epoch 14/50\n",
      "112720/112720 [==============================] - 1s - loss: 4.4441 - acc: 0.0049 - val_loss: 1671.7594 - val_acc: 0.0032\n",
      "Epoch 15/50\n",
      "112720/112720 [==============================] - 1s - loss: 4.4570 - acc: 0.0048 - val_loss: 1653.7028 - val_acc: 0.0031\n",
      "Epoch 16/50\n",
      "112720/112720 [==============================] - 1s - loss: 4.1199 - acc: 0.0050 - val_loss: 1645.9597 - val_acc: 0.0032\n",
      "Epoch 17/50\n",
      "112720/112720 [==============================] - 1s - loss: 4.2250 - acc: 0.0050 - val_loss: 1628.1361 - val_acc: 0.0030\n",
      "Epoch 18/50\n",
      "112720/112720 [==============================] - 1s - loss: 4.6163 - acc: 0.0049 - val_loss: 1601.0577 - val_acc: 0.0030\n",
      "Epoch 19/50\n",
      "112720/112720 [==============================] - 0s - loss: 4.2707 - acc: 0.0050 - val_loss: 1602.6514 - val_acc: 0.0030\n",
      "Epoch 20/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "112720/112720 [==============================] - 0s - loss: 4.2585 - acc: 0.0049 - val_loss: 1605.3748 - val_acc: 0.0031\n",
      "Epoch 21/50\n",
      "112720/112720 [==============================] - 1s - loss: 4.3591 - acc: 0.0049 - val_loss: 1580.4515 - val_acc: 0.0032\n",
      "Epoch 22/50\n",
      "112720/112720 [==============================] - 1s - loss: 4.1467 - acc: 0.0049 - val_loss: 1555.7949 - val_acc: 0.0032\n",
      "Epoch 23/50\n",
      "112720/112720 [==============================] - 1s - loss: 4.4550 - acc: 0.0049 - val_loss: 1527.0437 - val_acc: 0.0033\n",
      "Epoch 24/50\n",
      "112720/112720 [==============================] - 1s - loss: 4.0495 - acc: 0.0049 - val_loss: 1500.7872 - val_acc: 0.0034\n",
      "Epoch 25/50\n",
      "112720/112720 [==============================] - 1s - loss: 4.5215 - acc: 0.0049 - val_loss: 1493.7885 - val_acc: 0.0033\n",
      "Epoch 26/50\n",
      "112720/112720 [==============================] - 1s - loss: 4.0239 - acc: 0.0049 - val_loss: 1491.7838 - val_acc: 0.0034\n",
      "Epoch 27/50\n",
      "112720/112720 [==============================] - 1s - loss: 4.3339 - acc: 0.0048 - val_loss: 1458.9116 - val_acc: 0.0033\n",
      "Epoch 28/50\n",
      "112720/112720 [==============================] - 1s - loss: 4.2503 - acc: 0.0048 - val_loss: 1434.4523 - val_acc: 0.0033\n",
      "Epoch 29/50\n",
      "112720/112720 [==============================] - 1s - loss: 3.9268 - acc: 0.0048 - val_loss: 1419.0899 - val_acc: 0.0033\n",
      "Epoch 30/50\n",
      "112720/112720 [==============================] - 1s - loss: 3.8998 - acc: 0.0049 - val_loss: 1403.7219 - val_acc: 0.0033\n",
      "Epoch 31/50\n",
      "112720/112720 [==============================] - 1s - loss: 3.8895 - acc: 0.0048 - val_loss: 1397.8172 - val_acc: 0.0033\n",
      "Epoch 32/50\n",
      "112720/112720 [==============================] - 1s - loss: 3.9002 - acc: 0.0048 - val_loss: 1385.1864 - val_acc: 0.0033\n",
      "Epoch 33/50\n",
      "112720/112720 [==============================] - 1s - loss: 4.2100 - acc: 0.0049 - val_loss: 1364.7355 - val_acc: 0.0033\n",
      "Epoch 34/50\n",
      "112720/112720 [==============================] - 1s - loss: 3.9331 - acc: 0.0048 - val_loss: 1357.4559 - val_acc: 0.0033\n",
      "Epoch 35/50\n",
      "112720/112720 [==============================] - 1s - loss: 3.6526 - acc: 0.0049 - val_loss: 1353.1741 - val_acc: 0.0033\n",
      "Epoch 36/50\n",
      "112720/112720 [==============================] - 1s - loss: 3.9412 - acc: 0.0048 - val_loss: 1338.5161 - val_acc: 0.0033\n",
      "Epoch 37/50\n",
      "112720/112720 [==============================] - 1s - loss: 3.7562 - acc: 0.0049 - val_loss: 1323.6671 - val_acc: 0.0033\n",
      "Epoch 38/50\n",
      "112720/112720 [==============================] - 1s - loss: 3.7901 - acc: 0.0049 - val_loss: 1312.4930 - val_acc: 0.0033\n",
      "Epoch 39/50\n",
      "112720/112720 [==============================] - 1s - loss: 3.7277 - acc: 0.0049 - val_loss: 1300.0342 - val_acc: 0.0033\n",
      "Epoch 40/50\n",
      "112720/112720 [==============================] - 1s - loss: 3.8567 - acc: 0.0048 - val_loss: 1292.4708 - val_acc: 0.0033\n",
      "Epoch 41/50\n",
      "112720/112720 [==============================] - 1s - loss: 4.0073 - acc: 0.0050 - val_loss: 1291.5854 - val_acc: 0.0033\n",
      "Epoch 42/50\n",
      "112720/112720 [==============================] - 1s - loss: 3.7415 - acc: 0.0049 - val_loss: 1290.2688 - val_acc: 0.0033\n",
      "Epoch 43/50\n",
      "112720/112720 [==============================] - 1s - loss: 3.5056 - acc: 0.0049 - val_loss: 1284.5254 - val_acc: 0.0033\n",
      "Epoch 44/50\n",
      "112720/112720 [==============================] - 1s - loss: 3.5737 - acc: 0.0048 - val_loss: 1287.0718 - val_acc: 0.0033\n",
      "Epoch 45/50\n",
      "112720/112720 [==============================] - 1s - loss: 3.8756 - acc: 0.0048 - val_loss: 1287.6856 - val_acc: 0.0034\n",
      "Epoch 46/50\n",
      "112720/112720 [==============================] - 1s - loss: 3.5788 - acc: 0.0049 - val_loss: 1279.4116 - val_acc: 0.0034\n",
      "Epoch 47/50\n",
      "112720/112720 [==============================] - 1s - loss: 3.4611 - acc: 0.0048 - val_loss: 1277.5897 - val_acc: 0.0033\n",
      "Epoch 48/50\n",
      "112720/112720 [==============================] - 1s - loss: 3.3483 - acc: 0.0049 - val_loss: 1275.0434 - val_acc: 0.0033\n",
      "Epoch 49/50\n",
      "112720/112720 [==============================] - 1s - loss: 3.5038 - acc: 0.0048 - val_loss: 1273.0247 - val_acc: 0.0034\n",
      "Epoch 50/50\n",
      "112720/112720 [==============================] - 1s - loss: 3.7361 - acc: 0.0048 - val_loss: 1264.0871 - val_acc: 0.0033\n",
      "11272/11272 [==============================] - 0s     \n",
      "18317/22544 [=======================>......] - ETA: 0s\n",
      " Train Acc: 0.003992193087469786, Test Acc: 0.0033268275728914887\n",
      " \n",
      " Current Layer Attributes - epochs:50 hidden layers:2 features count:16\n",
      "Train on 112720 samples, validate on 22544 samples\n",
      "Epoch 1/50\n",
      "112720/112720 [==============================] - 2s - loss: 75870688567379.4844 - acc: 0.0291 - val_loss: 1651426455.7007 - val_acc: 0.0424\n",
      "Epoch 2/50\n",
      "112720/112720 [==============================] - 1s - loss: 152432965.3388 - acc: 0.0375 - val_loss: 467596690.7606 - val_acc: 0.0407\n",
      "Epoch 3/50\n",
      "112720/112720 [==============================] - 1s - loss: 141057036.8606 - acc: 0.0341 - val_loss: 354178759.5714 - val_acc: 0.0380\n",
      "Epoch 4/50\n",
      "112720/112720 [==============================] - 1s - loss: 137377798.1689 - acc: 0.0340 - val_loss: 318585413.6532 - val_acc: 0.0406\n",
      "Epoch 5/50\n",
      "112720/112720 [==============================] - 1s - loss: 135315453.2410 - acc: 0.0346 - val_loss: 294994399.2748 - val_acc: 0.0409\n",
      "Epoch 6/50\n",
      "112720/112720 [==============================] - 1s - loss: 133920729.7265 - acc: 0.0342 - val_loss: 274281554.5125 - val_acc: 0.0400\n",
      "Epoch 7/50\n",
      "112720/112720 [==============================] - 1s - loss: 132804385.5331 - acc: 0.0336 - val_loss: 263256503.4974 - val_acc: 0.0407\n",
      "Epoch 8/50\n",
      "112720/112720 [==============================] - 1s - loss: 131988574.0313 - acc: 0.0342 - val_loss: 247635797.0446 - val_acc: 0.0404\n",
      "Epoch 9/50\n",
      "112720/112720 [==============================] - 1s - loss: 131279808.3038 - acc: 0.0336 - val_loss: 237380694.8263 - val_acc: 0.0397\n",
      "Epoch 10/50\n",
      "112720/112720 [==============================] - 0s - loss: 130698247.4910 - acc: 0.0330 - val_loss: 228785958.1479 - val_acc: 0.0400\n",
      "Epoch 11/50\n",
      "112720/112720 [==============================] - 0s - loss: 130200661.3522 - acc: 0.0337 - val_loss: 221244341.5401 - val_acc: 0.0405\n",
      "Epoch 12/50\n",
      "112720/112720 [==============================] - 0s - loss: 129770475.6463 - acc: 0.0336 - val_loss: 214382498.2647 - val_acc: 0.0409\n",
      "Epoch 13/50\n",
      "112720/112720 [==============================] - 1s - loss: 129380230.0158 - acc: 0.0342 - val_loss: 208593978.6323 - val_acc: 0.0409\n",
      "Epoch 14/50\n",
      "112720/112720 [==============================] - 1s - loss: 129036642.5736 - acc: 0.0337 - val_loss: 203360043.5113 - val_acc: 0.0407\n",
      "Epoch 15/50\n",
      "112720/112720 [==============================] - 1s - loss: 128731453.6261 - acc: 0.0331 - val_loss: 198175237.7082 - val_acc: 0.0400\n",
      "Epoch 16/50\n",
      "112720/112720 [==============================] - 1s - loss: 128444715.5741 - acc: 0.0331 - val_loss: 193698000.3673 - val_acc: 0.0400\n",
      "Epoch 17/50\n",
      "112720/112720 [==============================] - 1s - loss: 128185695.8549 - acc: 0.0332 - val_loss: 189582955.9830 - val_acc: 0.0398\n",
      "Epoch 18/50\n",
      "112720/112720 [==============================] - 1s - loss: 127947749.3672 - acc: 0.0331 - val_loss: 185833051.0480 - val_acc: 0.0399\n",
      "Epoch 19/50\n",
      "112720/112720 [==============================] - 1s - loss: 127727841.5188 - acc: 0.0334 - val_loss: 182218232.3481 - val_acc: 0.0398\n",
      "Epoch 20/50\n",
      "112720/112720 [==============================] - 1s - loss: 127522882.8600 - acc: 0.0330 - val_loss: 180231539.0445 - val_acc: 0.0393\n",
      "Epoch 21/50\n",
      "112720/112720 [==============================] - 1s - loss: 127333447.8295 - acc: 0.0329 - val_loss: 175774323.9227 - val_acc: 0.0393\n",
      "Epoch 22/50\n",
      "112720/112720 [==============================] - 1s - loss: 127150431.7627 - acc: 0.0328 - val_loss: 173044992.8065 - val_acc: 0.0396\n",
      "Epoch 23/50\n",
      "112720/112720 [==============================] - 1s - loss: 126982157.0360 - acc: 0.0327 - val_loss: 170114288.0271 - val_acc: 0.0398\n",
      "Epoch 24/50\n",
      "112720/112720 [==============================] - 1s - loss: 126821629.0095 - acc: 0.0330 - val_loss: 168328650.2682 - val_acc: 0.0400\n",
      "Epoch 25/50\n",
      "112720/112720 [==============================] - 1s - loss: 126670226.1706 - acc: 0.0334 - val_loss: 165111777.0852 - val_acc: 0.0396\n",
      "Epoch 26/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "112720/112720 [==============================] - 1s - loss: 126527214.5422 - acc: 0.0328 - val_loss: 162605150.5627 - val_acc: 0.0396\n",
      "Epoch 27/50\n",
      "112720/112720 [==============================] - 1s - loss: 126389388.4845 - acc: 0.0331 - val_loss: 161924478.1718 - val_acc: 0.0397\n",
      "Epoch 28/50\n",
      "112720/112720 [==============================] - 1s - loss: 126261066.7085 - acc: 0.0330 - val_loss: 158296074.5017 - val_acc: 0.0396\n",
      "Epoch 29/50\n",
      "112720/112720 [==============================] - 1s - loss: 126132795.3147 - acc: 0.0332 - val_loss: 156276100.4415 - val_acc: 0.0397\n",
      "Epoch 30/50\n",
      "112720/112720 [==============================] - 1s - loss: 126013507.8034 - acc: 0.0332 - val_loss: 154699879.8382 - val_acc: 0.0398\n",
      "Epoch 31/50\n",
      "112720/112720 [==============================] - 1s - loss: 125899154.2454 - acc: 0.0330 - val_loss: 152655537.6534 - val_acc: 0.0399\n",
      "Epoch 32/50\n",
      "112720/112720 [==============================] - 1s - loss: 125796847.7556 - acc: 0.0328 - val_loss: 150702522.2640 - val_acc: 0.0397\n",
      "Epoch 33/50\n",
      "112720/112720 [==============================] - 1s - loss: 125681746.9778 - acc: 0.0328 - val_loss: 150311294.5109 - val_acc: 0.0394\n",
      "Epoch 34/50\n",
      "112720/112720 [==============================] - 1s - loss: 125579591.1565 - acc: 0.0325 - val_loss: 147357768.4010 - val_acc: 0.0394\n",
      "Epoch 35/50\n",
      "112720/112720 [==============================] - 1s - loss: 125480412.0933 - acc: 0.0327 - val_loss: 145708720.4109 - val_acc: 0.0393\n",
      "Epoch 36/50\n",
      "112720/112720 [==============================] - 1s - loss: 125411199.3108 - acc: 0.0323 - val_loss: 144209153.2925 - val_acc: 0.0390\n",
      "Epoch 37/50\n",
      "112720/112720 [==============================] - 1s - loss: 125293272.6721 - acc: 0.0326 - val_loss: 142891280.7871 - val_acc: 0.0390\n",
      "Epoch 38/50\n",
      "112720/112720 [==============================] - 1s - loss: 125204380.0693 - acc: 0.0324 - val_loss: 141268390.1815 - val_acc: 0.0392\n",
      "Epoch 39/50\n",
      "112720/112720 [==============================] - 1s - loss: 125117866.7563 - acc: 0.0326 - val_loss: 140464670.0183 - val_acc: 0.0391\n",
      "Epoch 40/50\n",
      "112720/112720 [==============================] - 1s - loss: 125033717.7557 - acc: 0.0324 - val_loss: 138533500.4600 - val_acc: 0.0391\n",
      "Epoch 41/50\n",
      "112720/112720 [==============================] - 1s - loss: 124952342.0105 - acc: 0.0326 - val_loss: 138279284.4861 - val_acc: 0.0391\n",
      "Epoch 42/50\n",
      "112720/112720 [==============================] - 1s - loss: 124895070.3501 - acc: 0.0328 - val_loss: 135887910.2294 - val_acc: 0.0393\n",
      "Epoch 43/50\n",
      "112720/112720 [==============================] - 1s - loss: 124795109.0584 - acc: 0.0328 - val_loss: 134635622.2227 - val_acc: 0.0394\n",
      "Epoch 44/50\n",
      "112720/112720 [==============================] - 1s - loss: 124720533.8681 - acc: 0.0328 - val_loss: 133501678.7357 - val_acc: 0.0394\n",
      "Epoch 45/50\n",
      "112720/112720 [==============================] - 1s - loss: 124647370.6928 - acc: 0.0326 - val_loss: 132308200.4476 - val_acc: 0.0392\n",
      "Epoch 46/50\n",
      "112720/112720 [==============================] - 1s - loss: 124576327.0191 - acc: 0.0332 - val_loss: 131168559.1061 - val_acc: 0.0394\n",
      "Epoch 47/50\n",
      "112720/112720 [==============================] - 1s - loss: 124507216.9774 - acc: 0.0329 - val_loss: 130069398.1873 - val_acc: 0.0395\n",
      "Epoch 48/50\n",
      "112720/112720 [==============================] - 1s - loss: 124439906.9408 - acc: 0.0332 - val_loss: 129055346.8111 - val_acc: 0.0394\n",
      "Epoch 49/50\n",
      "112720/112720 [==============================] - 1s - loss: 124374059.3165 - acc: 0.0328 - val_loss: 127949637.7896 - val_acc: 0.0393\n",
      "Epoch 50/50\n",
      "112720/112720 [==============================] - 1s - loss: 124308985.8709 - acc: 0.0326 - val_loss: 126960847.2479 - val_acc: 0.0393\n",
      "11272/11272 [==============================] - 0s     \n",
      "16908/22544 [=====================>........] - ETA: 0s\n",
      " Train Acc: 0.03442157548852265, Test Acc: 0.03925656480714679\n",
      " \n",
      " Current Layer Attributes - epochs:50 hidden layers:2 features count:32\n",
      "Train on 112720 samples, validate on 22544 samples\n",
      "Epoch 1/50\n",
      "112720/112720 [==============================] - 2s - loss: 97523351998138.8125 - acc: 0.0056 - val_loss: 543303473.5297 - val_acc: 4.8793e-04\n",
      "Epoch 2/50\n",
      "112720/112720 [==============================] - 1s - loss: 2824502.2567 - acc: 0.0053 - val_loss: 139416082.8380 - val_acc: 0.0013\n",
      "Epoch 3/50\n",
      "112720/112720 [==============================] - 1s - loss: 1162441.2408 - acc: 0.0055 - val_loss: 66426823.8068 - val_acc: 5.7665e-04\n",
      "Epoch 4/50\n",
      "112720/112720 [==============================] - 1s - loss: 885292.1961 - acc: 0.0052 - val_loss: 53772621.1870 - val_acc: 7.5408e-04\n",
      "Epoch 5/50\n",
      "112720/112720 [==============================] - 1s - loss: 768591.8930 - acc: 0.0054 - val_loss: 47425407.8808 - val_acc: 0.0010\n",
      "Epoch 6/50\n",
      "112720/112720 [==============================] - 1s - loss: 670796.7602 - acc: 0.0054 - val_loss: 41234906.3015 - val_acc: 0.0012\n",
      "Epoch 7/50\n",
      "112720/112720 [==============================] - 1s - loss: 610327.5583 - acc: 0.0055 - val_loss: 37323449.6863 - val_acc: 0.0013\n",
      "Epoch 8/50\n",
      "112720/112720 [==============================] - 1s - loss: 567354.3470 - acc: 0.0057 - val_loss: 34627865.6355 - val_acc: 0.0015\n",
      "Epoch 9/50\n",
      "112720/112720 [==============================] - 1s - loss: 534212.0904 - acc: 0.0057 - val_loss: 32423825.2270 - val_acc: 0.0015\n",
      "Epoch 10/50\n",
      "112720/112720 [==============================] - 1s - loss: 507624.6898 - acc: 0.0056 - val_loss: 30495638.0576 - val_acc: 0.0016\n",
      "Epoch 11/50\n",
      "112720/112720 [==============================] - 1s - loss: 485429.5653 - acc: 0.0057 - val_loss: 28858836.8709 - val_acc: 0.0016\n",
      "Epoch 12/50\n",
      "112720/112720 [==============================] - 1s - loss: 466430.9365 - acc: 0.0058 - val_loss: 27529943.6587 - val_acc: 0.0016\n",
      "Epoch 13/50\n",
      "112720/112720 [==============================] - 1s - loss: 449543.0614 - acc: 0.0058 - val_loss: 26518066.2848 - val_acc: 0.0016\n",
      "Epoch 14/50\n",
      "112720/112720 [==============================] - 1s - loss: 433820.1177 - acc: 0.0057 - val_loss: 25433406.1524 - val_acc: 0.0016\n",
      "Epoch 15/50\n",
      "112720/112720 [==============================] - 1s - loss: 420505.7631 - acc: 0.0057 - val_loss: 24631024.7269 - val_acc: 0.0016\n",
      "Epoch 16/50\n",
      "112720/112720 [==============================] - 1s - loss: 408357.9431 - acc: 0.0057 - val_loss: 23736095.3138 - val_acc: 0.0016\n",
      "Epoch 17/50\n",
      "112720/112720 [==============================] - 1s - loss: 397281.6559 - acc: 0.0057 - val_loss: 22936742.5737 - val_acc: 0.0016\n",
      "Epoch 18/50\n",
      "112720/112720 [==============================] - 1s - loss: 387221.7511 - acc: 0.0058 - val_loss: 22226792.6731 - val_acc: 0.0016\n",
      "Epoch 19/50\n",
      "112720/112720 [==============================] - 1s - loss: 377943.5579 - acc: 0.0057 - val_loss: 21549011.4846 - val_acc: 0.0016\n",
      "Epoch 20/50\n",
      "112720/112720 [==============================] - 1s - loss: 369227.0494 - acc: 0.0058 - val_loss: 21010289.4788 - val_acc: 0.0016\n",
      "Epoch 21/50\n",
      "112720/112720 [==============================] - 1s - loss: 361285.6973 - acc: 0.0057 - val_loss: 20418849.7102 - val_acc: 0.0016\n",
      "Epoch 22/50\n",
      "112720/112720 [==============================] - 1s - loss: 353612.6630 - acc: 0.0057 - val_loss: 19919065.6231 - val_acc: 0.0016\n",
      "Epoch 23/50\n",
      "112720/112720 [==============================] - 1s - loss: 346391.6478 - acc: 0.0057 - val_loss: 19516890.8429 - val_acc: 0.0016\n",
      "Epoch 24/50\n",
      "112720/112720 [==============================] - 1s - loss: 339657.1984 - acc: 0.0058 - val_loss: 18996441.2895 - val_acc: 0.0016\n",
      "Epoch 25/50\n",
      "112720/112720 [==============================] - 1s - loss: 333151.2782 - acc: 0.0058 - val_loss: 18660129.2428 - val_acc: 0.0016\n",
      "Epoch 26/50\n",
      "112720/112720 [==============================] - 1s - loss: 326986.4232 - acc: 0.0058 - val_loss: 18048920.9761 - val_acc: 0.0016\n",
      "Epoch 27/50\n",
      "112720/112720 [==============================] - 1s - loss: 321085.2685 - acc: 0.0057 - val_loss: 17760512.1920 - val_acc: 0.0016\n",
      "Epoch 28/50\n",
      "112720/112720 [==============================] - 1s - loss: 316141.2508 - acc: 0.0058 - val_loss: 17209957.7713 - val_acc: 0.0016\n",
      "Epoch 29/50\n",
      "112720/112720 [==============================] - 1s - loss: 310046.5298 - acc: 0.0057 - val_loss: 16845954.5845 - val_acc: 0.0016\n",
      "Epoch 30/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "112720/112720 [==============================] - 1s - loss: 304803.2948 - acc: 0.0058 - val_loss: 16441033.9308 - val_acc: 0.0016\n",
      "Epoch 31/50\n",
      "112720/112720 [==============================] - 1s - loss: 299770.2349 - acc: 0.0057 - val_loss: 16109049.0163 - val_acc: 0.0016\n",
      "Epoch 32/50\n",
      "112720/112720 [==============================] - 1s - loss: 295001.0043 - acc: 0.0058 - val_loss: 15832520.6417 - val_acc: 0.0016\n",
      "Epoch 33/50\n",
      "112720/112720 [==============================] - 1s - loss: 290533.6061 - acc: 0.0058 - val_loss: 15432839.2347 - val_acc: 0.0016\n",
      "Epoch 34/50\n",
      "112720/112720 [==============================] - 1s - loss: 285639.9410 - acc: 0.0058 - val_loss: 15166270.3033 - val_acc: 0.0016\n",
      "Epoch 35/50\n",
      "112720/112720 [==============================] - 1s - loss: 281223.8917 - acc: 0.0058 - val_loss: 14852928.6724 - val_acc: 0.0016\n",
      "Epoch 36/50\n",
      "112720/112720 [==============================] - 1s - loss: 276978.9954 - acc: 0.0058 - val_loss: 14553418.6116 - val_acc: 0.0016\n",
      "Epoch 37/50\n",
      "112720/112720 [==============================] - 1s - loss: 272728.9194 - acc: 0.0058 - val_loss: 14403982.6964 - val_acc: 0.0016\n",
      "Epoch 38/50\n",
      "112720/112720 [==============================] - 1s - loss: 268678.3597 - acc: 0.0058 - val_loss: 13996974.2427 - val_acc: 0.0016\n",
      "Epoch 39/50\n",
      "112720/112720 [==============================] - 1s - loss: 264667.7395 - acc: 0.0058 - val_loss: 13740164.7180 - val_acc: 0.0016\n",
      "Epoch 40/50\n",
      "112720/112720 [==============================] - 1s - loss: 260765.3113 - acc: 0.0058 - val_loss: 13523102.3876 - val_acc: 0.0016\n",
      "Epoch 41/50\n",
      "112720/112720 [==============================] - 1s - loss: 256964.8005 - acc: 0.0058 - val_loss: 13295165.9523 - val_acc: 0.0016\n",
      "Epoch 42/50\n",
      "112720/112720 [==============================] - 1s - loss: 253231.3640 - acc: 0.0058 - val_loss: 13103576.5612 - val_acc: 0.0016\n",
      "Epoch 43/50\n",
      "112720/112720 [==============================] - 1s - loss: 249568.8574 - acc: 0.0058 - val_loss: 12789887.4195 - val_acc: 0.0016\n",
      "Epoch 44/50\n",
      "112720/112720 [==============================] - 1s - loss: 246067.2841 - acc: 0.0059 - val_loss: 12559449.5610 - val_acc: 0.0016\n",
      "Epoch 45/50\n",
      "112720/112720 [==============================] - 1s - loss: 242496.1549 - acc: 0.0058 - val_loss: 12354069.7043 - val_acc: 0.0016\n",
      "Epoch 46/50\n",
      "112720/112720 [==============================] - 1s - loss: 239073.8115 - acc: 0.0058 - val_loss: 12148810.6690 - val_acc: 0.0016\n",
      "Epoch 47/50\n",
      "112720/112720 [==============================] - 1s - loss: 235724.1292 - acc: 0.0058 - val_loss: 11936305.5173 - val_acc: 0.0016\n",
      "Epoch 48/50\n",
      "112720/112720 [==============================] - 1s - loss: 232389.0287 - acc: 0.0058 - val_loss: 11731069.7958 - val_acc: 0.0016\n",
      "Epoch 49/50\n",
      "112720/112720 [==============================] - 1s - loss: 229223.7638 - acc: 0.0058 - val_loss: 11526543.7820 - val_acc: 0.0016\n",
      "Epoch 50/50\n",
      "112720/112720 [==============================] - 1s - loss: 225941.6035 - acc: 0.0059 - val_loss: 11323175.5369 - val_acc: 0.0016\n",
      "18317/22544 [=======================>......] - ETA: 0s\n",
      " Train Acc: 0.005056777910795063, Test Acc: 0.0015968772349879146\n",
      " \n",
      " Current Layer Attributes - epochs:50 hidden layers:6 features count:2\n",
      "Train on 112720 samples, validate on 22544 samples\n",
      "Epoch 1/50\n",
      "112720/112720 [==============================] - 4s - loss: 3.2168 - acc: 0.0049 - val_loss: 10.2027 - val_acc: 0.0010\n",
      "Epoch 2/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.6773 - acc: 0.0048 - val_loss: 11.2072 - val_acc: 0.0010\n",
      "Epoch 3/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7574 - acc: 0.0048 - val_loss: 10.8331 - val_acc: 0.0010\n",
      "Epoch 4/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5230 - acc: 0.0048 - val_loss: 10.6133 - val_acc: 0.0010\n",
      "Epoch 5/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5935 - acc: 0.0048 - val_loss: 11.0871 - val_acc: 0.0010\n",
      "Epoch 6/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.7649 - acc: 0.0048 - val_loss: 11.1409 - val_acc: 0.0010\n",
      "Epoch 7/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.9788 - acc: 0.0048 - val_loss: 10.9676 - val_acc: 0.0010\n",
      "Epoch 8/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.6596 - acc: 0.0048 - val_loss: 10.8551 - val_acc: 0.0010\n",
      "Epoch 9/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.8507 - acc: 0.0048 - val_loss: 10.9227 - val_acc: 0.0010\n",
      "Epoch 10/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.1039 - acc: 0.0048 - val_loss: 10.9838 - val_acc: 0.0010\n",
      "Epoch 11/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5887 - acc: 0.0049 - val_loss: 11.2483 - val_acc: 0.0010\n",
      "Epoch 12/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.4873 - acc: 0.0049 - val_loss: 11.1704 - val_acc: 0.0010\n",
      "Epoch 13/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.4190 - acc: 0.0048 - val_loss: 11.1720 - val_acc: 0.0010\n",
      "Epoch 14/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4897 - acc: 0.0049 - val_loss: 11.0735 - val_acc: 0.0010\n",
      "Epoch 15/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3682 - acc: 0.0048 - val_loss: 11.0330 - val_acc: 0.0010\n",
      "Epoch 16/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.9868 - acc: 0.0048 - val_loss: 10.9150 - val_acc: 0.0010\n",
      "Epoch 17/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.0281 - acc: 0.0048 - val_loss: 10.8480 - val_acc: 0.0010\n",
      "Epoch 18/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.3922 - acc: 0.0049 - val_loss: 11.0497 - val_acc: 0.0010\n",
      "Epoch 19/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.5498 - acc: 0.0049 - val_loss: 10.9573 - val_acc: 0.0010\n",
      "Epoch 20/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.9476 - acc: 0.0048 - val_loss: 11.0778 - val_acc: 0.0010\n",
      "Epoch 21/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.6973 - acc: 0.0048 - val_loss: 11.0908 - val_acc: 0.0010\n",
      "Epoch 22/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.8309 - acc: 0.0048 - val_loss: 11.1667 - val_acc: 0.0010\n",
      "Epoch 23/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.6329 - acc: 0.0048 - val_loss: 11.1051 - val_acc: 0.0010\n",
      "Epoch 24/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.9302 - acc: 0.0048 - val_loss: 11.1200 - val_acc: 0.0010\n",
      "Epoch 25/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.8528 - acc: 0.0048 - val_loss: 11.1187 - val_acc: 0.0010\n",
      "Epoch 26/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.9057 - acc: 0.0048 - val_loss: 11.1567 - val_acc: 0.0010\n",
      "Epoch 27/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.9424 - acc: 0.0048 - val_loss: 11.1871 - val_acc: 0.0010\n",
      "Epoch 28/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.5660 - acc: 0.0048 - val_loss: 11.2414 - val_acc: 0.0010\n",
      "Epoch 29/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7804 - acc: 0.0048 - val_loss: 11.2728 - val_acc: 0.0010\n",
      "Epoch 30/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.8424 - acc: 0.0048 - val_loss: 11.3016 - val_acc: 0.0010\n",
      "Epoch 31/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.1986 - acc: 0.0048 - val_loss: 11.3385 - val_acc: 0.0010\n",
      "Epoch 32/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.8193 - acc: 0.0048 - val_loss: 11.3321 - val_acc: 0.0010\n",
      "Epoch 33/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4575 - acc: 0.0048 - val_loss: 13.5864 - val_acc: 0.0010\n",
      "Epoch 34/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.4498 - acc: 0.0048 - val_loss: 13.4015 - val_acc: 0.0010\n",
      "Epoch 35/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2401 - acc: 0.0048 - val_loss: 13.2858 - val_acc: 0.0010\n",
      "Epoch 36/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4737 - acc: 0.0048 - val_loss: 13.2133 - val_acc: 0.0010\n",
      "Epoch 37/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3939 - acc: 0.0048 - val_loss: 13.2632 - val_acc: 0.0010\n",
      "Epoch 38/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3730 - acc: 0.0048 - val_loss: 13.3812 - val_acc: 0.0010\n",
      "Epoch 39/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "112720/112720 [==============================] - 2s - loss: 3.2777 - acc: 0.0048 - val_loss: 13.4220 - val_acc: 0.0010\n",
      "Epoch 40/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5627 - acc: 0.0048 - val_loss: 13.4151 - val_acc: 0.0010\n",
      "Epoch 41/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.6348 - acc: 0.0048 - val_loss: 13.4603 - val_acc: 0.0010\n",
      "Epoch 42/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.3917 - acc: 0.0048 - val_loss: 13.4534 - val_acc: 0.0010\n",
      "Epoch 43/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.8927 - acc: 0.0048 - val_loss: 13.4280 - val_acc: 0.0010\n",
      "Epoch 44/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.6107 - acc: 0.0048 - val_loss: 13.3602 - val_acc: 0.0010\n",
      "Epoch 45/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7247 - acc: 0.0048 - val_loss: 13.3988 - val_acc: 0.0010\n",
      "Epoch 46/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.4208 - acc: 0.0048 - val_loss: 13.3675 - val_acc: 0.0010\n",
      "Epoch 47/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4841 - acc: 0.0048 - val_loss: 13.4175 - val_acc: 0.0010\n",
      "Epoch 48/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.3846 - acc: 0.0048 - val_loss: 13.4236 - val_acc: 0.0010\n",
      "Epoch 49/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.1404 - acc: 0.0048 - val_loss: 13.4701 - val_acc: 0.0010\n",
      "Epoch 50/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.3020 - acc: 0.0048 - val_loss: 13.4803 - val_acc: 0.0010\n",
      "21135/22544 [===========================>..] - ETA: 0s\n",
      " Train Acc: 0.0044357700971886516, Test Acc: 0.0010202271223533899\n",
      " \n",
      " Current Layer Attributes - epochs:50 hidden layers:6 features count:4\n",
      "Train on 112720 samples, validate on 22544 samples\n",
      "Epoch 1/50\n",
      "112720/112720 [==============================] - 4s - loss: 3.2428 - acc: 3.5486e-05 - val_loss: 6.9254 - val_acc: 0.0000e+00\n",
      "Epoch 2/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5130 - acc: 3.5486e-05 - val_loss: 7.2321 - val_acc: 0.0000e+00\n",
      "Epoch 3/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.3115 - acc: 3.5486e-05 - val_loss: 7.2846 - val_acc: 0.0000e+00\n",
      "Epoch 4/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.0198 - acc: 3.5486e-05 - val_loss: 7.2990 - val_acc: 0.0000e+00\n",
      "Epoch 5/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7023 - acc: 3.5486e-05 - val_loss: 7.6748 - val_acc: 0.0000e+00\n",
      "Epoch 6/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.1597 - acc: 3.5486e-05 - val_loss: 7.4189 - val_acc: 0.0000e+00\n",
      "Epoch 7/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.9273 - acc: 3.5486e-05 - val_loss: 6.9370 - val_acc: 0.0000e+00\n",
      "Epoch 8/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.0934 - acc: 3.5486e-05 - val_loss: 7.2137 - val_acc: 0.0000e+00\n",
      "Epoch 9/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.5399 - acc: 3.5486e-05 - val_loss: 7.2931 - val_acc: 0.0000e+00\n",
      "Epoch 10/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.5803 - acc: 3.5486e-05 - val_loss: 7.4186 - val_acc: 0.0000e+00\n",
      "Epoch 11/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.3710 - acc: 3.5486e-05 - val_loss: 7.5018 - val_acc: 0.0000e+00\n",
      "Epoch 12/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.0649 - acc: 3.5486e-05 - val_loss: 7.3372 - val_acc: 0.0000e+00\n",
      "Epoch 13/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.0732 - acc: 3.5486e-05 - val_loss: 7.2852 - val_acc: 0.0000e+0089e\n",
      "Epoch 14/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4575 - acc: 3.5486e-05 - val_loss: 7.3688 - val_acc: 0.0000e+00\n",
      "Epoch 15/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.9365 - acc: 3.5486e-05 - val_loss: 7.0900 - val_acc: 0.0000e+00\n",
      "Epoch 16/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.6690 - acc: 3.5486e-05 - val_loss: 6.9786 - val_acc: 0.0000e+00\n",
      "Epoch 17/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7198 - acc: 3.5486e-05 - val_loss: 7.0062 - val_acc: 0.0000e+00\n",
      "Epoch 18/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7893 - acc: 3.5486e-05 - val_loss: 7.0176 - val_acc: 0.0000e+00\n",
      "Epoch 19/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.3192 - acc: 3.5486e-05 - val_loss: 6.9564 - val_acc: 0.0000e+00\n",
      "Epoch 20/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.5454 - acc: 3.5486e-05 - val_loss: 6.9683 - val_acc: 0.0000e+00\n",
      "Epoch 21/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.8587 - acc: 3.5486e-05 - val_loss: 6.9018 - val_acc: 0.0000e+00\n",
      "Epoch 22/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.3540 - acc: 3.5486e-05 - val_loss: 6.9521 - val_acc: 0.0000e+00\n",
      "Epoch 23/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.4607 - acc: 3.5486e-05 - val_loss: 6.7573 - val_acc: 0.0000e+00\n",
      "Epoch 24/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.6211 - acc: 3.5486e-05 - val_loss: 6.7963 - val_acc: 0.0000e+00\n",
      "Epoch 25/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.2164 - acc: 3.5486e-05 - val_loss: 6.7469 - val_acc: 0.0000e+00\n",
      "Epoch 26/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.8131 - acc: 3.5486e-05 - val_loss: 6.6782 - val_acc: 0.0000e+00\n",
      "Epoch 27/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.6636 - acc: 3.5486e-05 - val_loss: 6.6365 - val_acc: 0.0000e+00\n",
      "Epoch 28/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.3500 - acc: 3.5486e-05 - val_loss: 6.6722 - val_acc: 0.0000e+00\n",
      "Epoch 29/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.5608 - acc: 3.5486e-05 - val_loss: 6.7144 - val_acc: 0.0000e+00\n",
      "Epoch 30/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.7452 - acc: 3.5486e-05 - val_loss: 6.7805 - val_acc: 0.0000e+00\n",
      "Epoch 31/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.8819 - acc: 3.5486e-05 - val_loss: 6.8239 - val_acc: 0.0000e+00\n",
      "Epoch 32/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.6046 - acc: 3.5486e-05 - val_loss: 6.9141 - val_acc: 0.0000e+00\n",
      "Epoch 33/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4433 - acc: 3.5486e-05 - val_loss: 7.1001 - val_acc: 0.0000e+00\n",
      "Epoch 34/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.9581 - acc: 3.5486e-05 - val_loss: 7.1199 - val_acc: 0.0000e+00\n",
      "Epoch 35/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3125 - acc: 3.5486e-05 - val_loss: 7.1545 - val_acc: 0.0000e+00\n",
      "Epoch 36/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.6731 - acc: 3.5486e-05 - val_loss: 7.8672 - val_acc: 0.0000e+00\n",
      "Epoch 37/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4202 - acc: 3.5486e-05 - val_loss: 7.5562 - val_acc: 0.0000e+00\n",
      "Epoch 38/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.7534 - acc: 3.5486e-05 - val_loss: 8.4623 - val_acc: 0.0000e+00\n",
      "Epoch 39/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5560 - acc: 3.5486e-05 - val_loss: 8.4865 - val_acc: 0.0000e+00\n",
      "Epoch 40/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5719 - acc: 3.5486e-05 - val_loss: 8.1654 - val_acc: 0.0000e+00\n",
      "Epoch 41/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.6967 - acc: 3.5486e-05 - val_loss: 7.6060 - val_acc: 0.0000e+00\n",
      "Epoch 42/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.8341 - acc: 3.5486e-05 - val_loss: 7.7578 - val_acc: 0.0000e+00\n",
      "Epoch 43/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.8270 - acc: 3.5486e-05 - val_loss: 8.2452 - val_acc: 0.0000e+00\n",
      "Epoch 44/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3558 - acc: 3.5486e-05 - val_loss: 8.0664 - val_acc: 0.0000e+00\n",
      "Epoch 45/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5987 - acc: 3.5486e-05 - val_loss: 8.2160 - val_acc: 0.0000e+00\n",
      "Epoch 46/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.9732 - acc: 3.5486e-05 - val_loss: 8.0835 - val_acc: 0.0000e+00\n",
      "Epoch 47/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "112720/112720 [==============================] - 2s - loss: 2.6443 - acc: 3.5486e-05 - val_loss: 8.1118 - val_acc: 0.0000e+00\n",
      "Epoch 48/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4232 - acc: 3.5486e-05 - val_loss: 8.1254 - val_acc: 0.0000e+00\n",
      "Epoch 49/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3911 - acc: 3.5486e-05 - val_loss: 7.9745 - val_acc: 0.0000e+00\n",
      "Epoch 50/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5178 - acc: 3.5486e-05 - val_loss: 7.8652 - val_acc: 0.0000e+00\n",
      "21135/22544 [===========================>..] - ETA: 0s\n",
      " Train Acc: 0.0, Test Acc: 0.0\n",
      " \n",
      " Current Layer Attributes - epochs:50 hidden layers:6 features count:8\n",
      "Train on 112720 samples, validate on 22544 samples\n",
      "Epoch 1/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.2869 - acc: 0.0013 - val_loss: 24.6740 - val_acc: 0.0000e+00\n",
      "Epoch 2/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.0350 - acc: 0.0000e+00 - val_loss: 23.8166 - val_acc: 0.0000e+00\n",
      "Epoch 3/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.6068 - acc: 0.0000e+00 - val_loss: 23.7051 - val_acc: 0.0000e+00\n",
      "Epoch 4/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2622 - acc: 0.0000e+00 - val_loss: 23.6932 - val_acc: 0.0000e+00\n",
      "Epoch 5/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3449 - acc: 0.0000e+00 - val_loss: 23.6972 - val_acc: 0.0000e+00\n",
      "Epoch 6/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3501 - acc: 8.8715e-06 - val_loss: 24.0043 - val_acc: 0.0000e+00\n",
      "Epoch 7/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2036 - acc: 0.0000e+00 - val_loss: 23.9400 - val_acc: 0.0000e+00\n",
      "Epoch 8/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2916 - acc: 8.8715e-06 - val_loss: 23.8503 - val_acc: 0.0000e+00\n",
      "Epoch 9/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3865 - acc: 8.8715e-06 - val_loss: 23.7552 - val_acc: 0.0000e+00\n",
      "Epoch 10/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5873 - acc: 2.6615e-05 - val_loss: 23.7735 - val_acc: 0.0000e+00\n",
      "Epoch 11/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2295 - acc: 3.5486e-05 - val_loss: 23.5842 - val_acc: 4.4358e-05\n",
      "Epoch 12/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.1066 - acc: 8.8715e-06 - val_loss: 23.6831 - val_acc: 4.4358e-05\n",
      "Epoch 13/50\n",
      "112720/112720 [==============================] - 2s - loss: 1.9772 - acc: 3.5486e-05 - val_loss: 23.6346 - val_acc: 4.4358e-05\n",
      "Epoch 14/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3247 - acc: 7.0972e-05 - val_loss: 23.5998 - val_acc: 4.4358e-05\n",
      "Epoch 15/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4369 - acc: 7.0972e-05 - val_loss: 23.5537 - val_acc: 4.4358e-05\n",
      "Epoch 16/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4654 - acc: 4.4358e-05 - val_loss: 23.7134 - val_acc: 4.4358e-05\n",
      "Epoch 17/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.1115 - acc: 6.2101e-05 - val_loss: 23.6479 - val_acc: 4.4358e-05\n",
      "Epoch 18/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4610 - acc: 2.6615e-05 - val_loss: 23.7284 - val_acc: 4.4358e-05\n",
      "Epoch 19/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2538 - acc: 8.8715e-05 - val_loss: 23.7575 - val_acc: 4.4358e-05\n",
      "Epoch 20/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2077 - acc: 9.7587e-05 - val_loss: 23.7596 - val_acc: 4.4358e-05\n",
      "Epoch 21/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.0733 - acc: 9.7587e-05 - val_loss: 23.8288 - val_acc: 4.4358e-05\n",
      "Epoch 22/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.2539 - acc: 1.5082e-04 - val_loss: 23.8088 - val_acc: 4.4358e-05\n",
      "Epoch 23/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.6151 - acc: 1.0646e-04 - val_loss: 23.7883 - val_acc: 4.4358e-05\n",
      "Epoch 24/50\n",
      "112720/112720 [==============================] - 2s - loss: 1.8024 - acc: 1.5969e-04 - val_loss: 23.7927 - val_acc: 4.4358e-05\n",
      "Epoch 25/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3246 - acc: 1.9517e-04 - val_loss: 23.7832 - val_acc: 4.4358e-05\n",
      "Epoch 26/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.1947 - acc: 2.7502e-04 - val_loss: 23.8102 - val_acc: 4.4358e-05\n",
      "Epoch 27/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2222 - acc: 3.1938e-04 - val_loss: 23.7943 - val_acc: 4.4358e-05\n",
      "Epoch 28/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.1254 - acc: 3.1938e-04 - val_loss: 23.8112 - val_acc: 4.4358e-05\n",
      "Epoch 29/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3002 - acc: 3.9035e-04 - val_loss: 23.8418 - val_acc: 4.4358e-05\n",
      "Epoch 30/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4115 - acc: 3.1050e-04 - val_loss: 23.8319 - val_acc: 4.4358e-05\n",
      "Epoch 31/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4211 - acc: 3.4599e-04 - val_loss: 23.7853 - val_acc: 4.4358e-05\n",
      "Epoch 32/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.0874 - acc: 3.3712e-04 - val_loss: 23.7726 - val_acc: 4.4358e-05\n",
      "Epoch 33/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4820 - acc: 4.1696e-04 - val_loss: 23.7564 - val_acc: 4.4358e-05\n",
      "Epoch 34/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.1717 - acc: 3.9922e-04 - val_loss: 23.7488 - val_acc: 4.4358e-05\n",
      "Epoch 35/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5438 - acc: 4.7906e-04 - val_loss: 23.7668 - val_acc: 4.4358e-05\n",
      "Epoch 36/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3809 - acc: 4.5245e-04 - val_loss: 23.7366 - val_acc: 4.4358e-052e\n",
      "Epoch 37/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.0744 - acc: 4.5245e-04 - val_loss: 23.7512 - val_acc: 4.4358e-05\n",
      "Epoch 38/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3339 - acc: 4.1696e-04 - val_loss: 23.7302 - val_acc: 4.4358e-05\n",
      "Epoch 39/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4419 - acc: 5.3229e-04 - val_loss: 23.7169 - val_acc: 4.4358e-05\n",
      "Epoch 40/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5426 - acc: 5.5004e-04 - val_loss: 23.7394 - val_acc: 4.4358e-05\n",
      "Epoch 41/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2900 - acc: 4.7906e-04 - val_loss: 23.7566 - val_acc: 4.4358e-05\n",
      "Epoch 42/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3971 - acc: 4.8793e-04 - val_loss: 23.7169 - val_acc: 8.8715e-05\n",
      "Epoch 43/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3387 - acc: 5.3229e-04 - val_loss: 23.7602 - val_acc: 8.8715e-05\n",
      "Epoch 44/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4733 - acc: 4.8793e-04 - val_loss: 23.7696 - val_acc: 8.8715e-05\n",
      "Epoch 45/50\n",
      "112720/112720 [==============================] - 2s - loss: 1.9831 - acc: 6.0326e-04 - val_loss: 23.8059 - val_acc: 8.8715e-05\n",
      "Epoch 46/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.0795 - acc: 5.3229e-04 - val_loss: 23.8078 - val_acc: 8.8715e-05\n",
      "Epoch 47/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2592 - acc: 6.2101e-04 - val_loss: 23.8476 - val_acc: 8.8715e-05\n",
      "Epoch 48/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2437 - acc: 5.5004e-04 - val_loss: 23.8781 - val_acc: 8.8715e-05\n",
      "Epoch 49/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2248 - acc: 5.9439e-04 - val_loss: 23.8838 - val_acc: 8.8715e-05\n",
      "Epoch 50/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3514 - acc: 6.0326e-04 - val_loss: 23.8619 - val_acc: 8.8715e-05\n",
      "21135/22544 [===========================>..] - ETA: 0s\n",
      " Train Acc: 0.0007984386174939573, Test Acc: 8.871540194377303e-05\n",
      " \n",
      " Current Layer Attributes - epochs:50 hidden layers:6 features count:16\n",
      "Train on 112720 samples, validate on 22544 samples\n",
      "Epoch 1/50\n",
      "112720/112720 [==============================] - 4s - loss: 3.5993 - acc: 0.0018 - val_loss: 33.6889 - val_acc: 0.0012\n",
      "Epoch 2/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "112720/112720 [==============================] - 2s - loss: 2.3932 - acc: 0.0037 - val_loss: 37.3356 - val_acc: 0.0012\n",
      "Epoch 3/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.7086 - acc: 0.0037 - val_loss: 36.3345 - val_acc: 0.0012\n",
      "Epoch 4/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.6416 - acc: 0.0037 - val_loss: 36.3767 - val_acc: 0.0012\n",
      "Epoch 5/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.1955 - acc: 0.0037 - val_loss: 36.4139 - val_acc: 0.0012\n",
      "Epoch 6/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3728 - acc: 0.0037 - val_loss: 36.3579 - val_acc: 0.0012\n",
      "Epoch 7/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2846 - acc: 0.0037 - val_loss: 36.4819 - val_acc: 0.0012\n",
      "Epoch 8/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4135 - acc: 0.0037 - val_loss: 36.3652 - val_acc: 0.0012\n",
      "Epoch 9/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4287 - acc: 0.0037 - val_loss: 36.5787 - val_acc: 0.0012\n",
      "Epoch 10/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4924 - acc: 0.0037 - val_loss: 36.5134 - val_acc: 0.0012\n",
      "Epoch 11/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3991 - acc: 0.0037 - val_loss: 36.6105 - val_acc: 0.0012\n",
      "Epoch 12/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.1617 - acc: 0.0037 - val_loss: 36.7004 - val_acc: 0.0012\n",
      "Epoch 13/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.6739 - acc: 0.0037 - val_loss: 36.6873 - val_acc: 0.0012\n",
      "Epoch 14/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3973 - acc: 0.0037 - val_loss: 36.9504 - val_acc: 0.0012\n",
      "Epoch 15/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5098 - acc: 0.0037 - val_loss: 36.9160 - val_acc: 0.0012\n",
      "Epoch 16/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.7636 - acc: 0.0037 - val_loss: 36.8031 - val_acc: 0.0012\n",
      "Epoch 17/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.2260 - acc: 0.0037 - val_loss: 36.9733 - val_acc: 0.0012\n",
      "Epoch 18/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.4730 - acc: 0.0037 - val_loss: 36.6417 - val_acc: 0.0012\n",
      "Epoch 19/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.0326 - acc: 0.0037 - val_loss: 36.6668 - val_acc: 0.0012\n",
      "Epoch 20/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.8388 - acc: 0.0037 - val_loss: 36.6129 - val_acc: 0.0012\n",
      "Epoch 21/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.1488 - acc: 0.0037 - val_loss: 36.4943 - val_acc: 0.0012\n",
      "Epoch 22/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.1048 - acc: 0.0037 - val_loss: 36.4652 - val_acc: 0.0012\n",
      "Epoch 23/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.8284 - acc: 0.0037 - val_loss: 36.4142 - val_acc: 0.0012\n",
      "Epoch 24/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.1402 - acc: 0.0037 - val_loss: 36.4526 - val_acc: 0.0012\n",
      "Epoch 25/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.6234 - acc: 0.0037 - val_loss: 36.4674 - val_acc: 0.0012\n",
      "Epoch 26/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2918 - acc: 0.0037 - val_loss: 36.3799 - val_acc: 0.0012\n",
      "Epoch 27/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.7444 - acc: 0.0037 - val_loss: 36.4468 - val_acc: 0.0012\n",
      "Epoch 28/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5813 - acc: 0.0037 - val_loss: 36.3517 - val_acc: 0.0012\n",
      "Epoch 29/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2396 - acc: 0.0037 - val_loss: 36.3100 - val_acc: 0.0012\n",
      "Epoch 30/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3807 - acc: 0.0037 - val_loss: 36.2532 - val_acc: 0.0012\n",
      "Epoch 31/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4271 - acc: 0.0037 - val_loss: 36.2585 - val_acc: 0.0012\n",
      "Epoch 32/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3650 - acc: 0.0037 - val_loss: 36.1844 - val_acc: 0.0012\n",
      "Epoch 33/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3600 - acc: 0.0037 - val_loss: 36.2235 - val_acc: 0.0012\n",
      "Epoch 34/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2868 - acc: 0.0037 - val_loss: 36.1987 - val_acc: 0.0012\n",
      "Epoch 35/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.0720 - acc: 0.0037 - val_loss: 36.2856 - val_acc: 0.0012\n",
      "Epoch 36/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2835 - acc: 0.0037 - val_loss: 36.2615 - val_acc: 0.0012\n",
      "Epoch 37/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2268 - acc: 0.0037 - val_loss: 36.3033 - val_acc: 0.0012\n",
      "Epoch 38/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4920 - acc: 0.0037 - val_loss: 36.3073 - val_acc: 0.0012\n",
      "Epoch 39/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4634 - acc: 0.0037 - val_loss: 36.3714 - val_acc: 0.0012\n",
      "Epoch 40/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.0936 - acc: 0.0037 - val_loss: 36.3766 - val_acc: 0.0012\n",
      "Epoch 41/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.1017 - acc: 0.0037 - val_loss: 36.3950 - val_acc: 0.0012\n",
      "Epoch 42/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5330 - acc: 0.0037 - val_loss: 36.4475 - val_acc: 0.0012\n",
      "Epoch 43/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2887 - acc: 0.0037 - val_loss: 36.3916 - val_acc: 0.0012\n",
      "Epoch 44/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2652 - acc: 0.0037 - val_loss: 36.3575 - val_acc: 0.0012\n",
      "Epoch 45/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4765 - acc: 0.0037 - val_loss: 36.3481 - val_acc: 0.0012\n",
      "Epoch 46/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3045 - acc: 0.0037 - val_loss: 36.3430 - val_acc: 0.0012\n",
      "Epoch 47/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.1359 - acc: 0.0037 - val_loss: 36.3320 - val_acc: 0.0012\n",
      "Epoch 48/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4799 - acc: 0.0037 - val_loss: 36.2558 - val_acc: 0.0012\n",
      "Epoch 49/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3105 - acc: 0.0037 - val_loss: 36.3112 - val_acc: 0.0012\n",
      "Epoch 50/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3863 - acc: 0.0037 - val_loss: 36.2627 - val_acc: 0.0012\n",
      "18317/22544 [=======================>......] - ETA: 0s\n",
      " Train Acc: 0.0040809084894135594, Test Acc: 0.001197657926240936\n",
      " \n",
      " Current Layer Attributes - epochs:50 hidden layers:6 features count:32\n",
      "Train on 112720 samples, validate on 22544 samples\n",
      "Epoch 1/50\n",
      "112720/112720 [==============================] - 4s - loss: 2.5417 - acc: 0.0041 - val_loss: 16.4292 - val_acc: 0.0116\n",
      "Epoch 2/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.1188 - acc: 0.0046 - val_loss: 16.9629 - val_acc: 0.0116\n",
      "Epoch 3/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2265 - acc: 0.0046 - val_loss: 16.2175 - val_acc: 0.0116\n",
      "Epoch 4/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3136 - acc: 0.0045 - val_loss: 16.3083 - val_acc: 0.0116\n",
      "Epoch 5/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4890 - acc: 0.0045 - val_loss: 16.5067 - val_acc: 0.0116\n",
      "Epoch 6/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.0506 - acc: 0.0045 - val_loss: 16.2166 - val_acc: 0.0116\n",
      "Epoch 7/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2869 - acc: 0.0044 - val_loss: 16.3406 - val_acc: 0.0115\n",
      "Epoch 8/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.0679 - acc: 0.0044 - val_loss: 16.1239 - val_acc: 0.0115\n",
      "Epoch 9/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2862 - acc: 0.0043 - val_loss: 16.1655 - val_acc: 0.0115\n",
      "Epoch 10/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4694 - acc: 0.0044 - val_loss: 16.1934 - val_acc: 0.0115\n",
      "Epoch 11/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.0136 - acc: 0.0043 - val_loss: 16.3230 - val_acc: 0.0115\n",
      "Epoch 12/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "112720/112720 [==============================] - 2s - loss: 2.2663 - acc: 0.0044 - val_loss: 16.2838 - val_acc: 0.0114\n",
      "Epoch 13/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2100 - acc: 0.0041 - val_loss: 16.3530 - val_acc: 0.0113\n",
      "Epoch 14/50\n",
      "112720/112720 [==============================] - 2s - loss: 1.9781 - acc: 0.0040 - val_loss: 16.2449 - val_acc: 0.0112\n",
      "Epoch 15/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.1380 - acc: 0.0041 - val_loss: 16.2878 - val_acc: 0.0111\n",
      "Epoch 16/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.0578 - acc: 0.0039 - val_loss: 16.1554 - val_acc: 0.0110\n",
      "Epoch 17/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2773 - acc: 0.0039 - val_loss: 16.2945 - val_acc: 0.0109\n",
      "Epoch 18/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.1112 - acc: 0.0038 - val_loss: 16.2549 - val_acc: 0.0108\n",
      "Epoch 19/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2830 - acc: 0.0039 - val_loss: 16.2605 - val_acc: 0.0110\n",
      "Epoch 20/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3488 - acc: 0.0040 - val_loss: 16.2908 - val_acc: 0.0111\n",
      "Epoch 21/50\n",
      "112720/112720 [==============================] - 2s - loss: 1.9388 - acc: 0.0039 - val_loss: 16.3042 - val_acc: 0.0111\n",
      "Epoch 22/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3540 - acc: 0.0039 - val_loss: 16.3888 - val_acc: 0.0111\n",
      "Epoch 23/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3955 - acc: 0.0039 - val_loss: 16.3776 - val_acc: 0.0110\n",
      "Epoch 24/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.1903 - acc: 0.0040 - val_loss: 16.4438 - val_acc: 0.0111\n",
      "Epoch 25/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.1961 - acc: 0.0039 - val_loss: 16.4461 - val_acc: 0.0112\n",
      "Epoch 26/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.0869 - acc: 0.0041 - val_loss: 16.3904 - val_acc: 0.0113\n",
      "Epoch 27/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5238 - acc: 0.0041 - val_loss: 16.3587 - val_acc: 0.0111\n",
      "Epoch 28/50\n",
      "112720/112720 [==============================] - 2s - loss: 1.8198 - acc: 0.0041 - val_loss: 16.3130 - val_acc: 0.0112\n",
      "Epoch 29/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.1478 - acc: 0.0041 - val_loss: 16.2408 - val_acc: 0.0111\n",
      "Epoch 30/50\n",
      "112720/112720 [==============================] - 1s - loss: 1.9437 - acc: 0.0040 - val_loss: 16.1758 - val_acc: 0.0109\n",
      "Epoch 31/50\n",
      "112720/112720 [==============================] - 1s - loss: 2.2279 - acc: 0.0039 - val_loss: 16.1732 - val_acc: 0.0111\n",
      "Epoch 32/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3296 - acc: 0.0041 - val_loss: 16.2148 - val_acc: 0.0111\n",
      "Epoch 33/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.1437 - acc: 0.0039 - val_loss: 16.1854 - val_acc: 0.0110\n",
      "Epoch 34/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.0771 - acc: 0.0039 - val_loss: 16.1353 - val_acc: 0.0108\n",
      "Epoch 35/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.1415 - acc: 0.0040 - val_loss: 16.1177 - val_acc: 0.0108\n",
      "Epoch 36/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.0024 - acc: 0.0039 - val_loss: 16.0797 - val_acc: 0.0107\n",
      "Epoch 37/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.1003 - acc: 0.0038 - val_loss: 16.0729 - val_acc: 0.0106\n",
      "Epoch 38/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3208 - acc: 0.0039 - val_loss: 16.1185 - val_acc: 0.0106\n",
      "Epoch 39/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.1630 - acc: 0.0038 - val_loss: 16.1783 - val_acc: 0.0105\n",
      "Epoch 40/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4297 - acc: 0.0036 - val_loss: 16.1884 - val_acc: 0.0106\n",
      "Epoch 41/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2336 - acc: 0.0037 - val_loss: 16.1949 - val_acc: 0.0105\n",
      "Epoch 42/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4104 - acc: 0.0037 - val_loss: 16.1997 - val_acc: 0.0106\n",
      "Epoch 43/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.1839 - acc: 0.0038 - val_loss: 16.1944 - val_acc: 0.0106\n",
      "Epoch 44/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4861 - acc: 0.0039 - val_loss: 16.1712 - val_acc: 0.0107\n",
      "Epoch 45/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2086 - acc: 0.0038 - val_loss: 16.1578 - val_acc: 0.0106\n",
      "Epoch 46/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.0404 - acc: 0.0039 - val_loss: 16.1727 - val_acc: 0.0103\n",
      "Epoch 47/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.0288 - acc: 0.0036 - val_loss: 16.1740 - val_acc: 0.0102\n",
      "Epoch 48/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2673 - acc: 0.0036 - val_loss: 16.1912 - val_acc: 0.0102\n",
      "Epoch 49/50\n",
      "112720/112720 [==============================] - 2s - loss: 1.9884 - acc: 0.0036 - val_loss: 16.1511 - val_acc: 0.0104\n",
      "Epoch 50/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4406 - acc: 0.0035 - val_loss: 16.1780 - val_acc: 0.0104\n",
      "18317/22544 [=======================>......] - ETA: 0s\n",
      " Train Acc: 0.0039034776855260134, Test Acc: 0.010424059524666518\n",
      " \n",
      " Current Layer Attributes - epochs:50 hidden layers:10 features count:2\n",
      "Train on 112720 samples, validate on 22544 samples\n",
      "Epoch 1/50\n",
      "112720/112720 [==============================] - 6s - loss: 2.7526 - acc: 0.0027 - val_loss: 6.8182 - val_acc: 7.9844e-04\n",
      "Epoch 2/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.2387 - acc: 0.0026 - val_loss: 6.7179 - val_acc: 7.9844e-04\n",
      "Epoch 3/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.6328 - acc: 0.0026 - val_loss: 6.7826 - val_acc: 7.9844e-04\n",
      "Epoch 4/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.1965 - acc: 0.0026 - val_loss: 6.7663 - val_acc: 7.9844e-04\n",
      "Epoch 5/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.5573 - acc: 0.0026 - val_loss: 6.6532 - val_acc: 7.9844e-04\n",
      "Epoch 6/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.0095 - acc: 0.0026 - val_loss: 6.5637 - val_acc: 7.9844e-04\n",
      "Epoch 7/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.0438 - acc: 0.0026 - val_loss: 6.5303 - val_acc: 7.9844e-04\n",
      "Epoch 8/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.4851 - acc: 0.0026 - val_loss: 6.3943 - val_acc: 7.9844e-04\n",
      "Epoch 9/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.9599 - acc: 0.0026 - val_loss: 6.2872 - val_acc: 7.9844e-04\n",
      "Epoch 10/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7448 - acc: 0.0026 - val_loss: 6.1831 - val_acc: 7.9844e-04\n",
      "Epoch 11/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.3249 - acc: 0.0026 - val_loss: 5.4694 - val_acc: 7.9844e-04\n",
      "Epoch 12/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.9512 - acc: 0.0026 - val_loss: 5.0449 - val_acc: 7.9844e-04\n",
      "Epoch 13/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.9163 - acc: 0.0026 - val_loss: 5.1541 - val_acc: 7.9844e-04\n",
      "Epoch 14/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.8842 - acc: 0.0026 - val_loss: 4.9546 - val_acc: 7.9844e-04\n",
      "Epoch 15/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7927 - acc: 0.0026 - val_loss: 4.8798 - val_acc: 7.9844e-04\n",
      "Epoch 16/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.3183 - acc: 0.0026 - val_loss: 4.6705 - val_acc: 7.9844e-04\n",
      "Epoch 17/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7919 - acc: 0.0026 - val_loss: 4.6544 - val_acc: 7.9844e-04\n",
      "Epoch 18/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.8964 - acc: 0.0026 - val_loss: 5.0088 - val_acc: 7.9844e-04\n",
      "Epoch 19/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.6304 - acc: 0.0026 - val_loss: 4.7279 - val_acc: 7.9844e-04\n",
      "Epoch 20/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.5121 - acc: 0.0026 - val_loss: 4.7516 - val_acc: 7.9844e-04\n",
      "Epoch 21/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.1743 - acc: 0.0026 - val_loss: 4.6455 - val_acc: 7.9844e-04\n",
      "Epoch 22/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "112720/112720 [==============================] - 3s - loss: 2.7026 - acc: 0.0026 - val_loss: 4.4676 - val_acc: 7.9844e-04\n",
      "Epoch 23/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.3954 - acc: 0.0026 - val_loss: 4.4979 - val_acc: 7.9844e-04\n",
      "Epoch 24/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7072 - acc: 0.0026 - val_loss: 4.5249 - val_acc: 7.9844e-04\n",
      "Epoch 25/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.6389 - acc: 0.0026 - val_loss: 4.5545 - val_acc: 7.9844e-04\n",
      "Epoch 26/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7775 - acc: 0.0026 - val_loss: 4.5916 - val_acc: 7.9844e-04\n",
      "Epoch 27/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.8940 - acc: 0.0026 - val_loss: 4.6317 - val_acc: 7.9844e-04\n",
      "Epoch 28/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.4215 - acc: 0.0026 - val_loss: 4.7347 - val_acc: 7.9844e-04\n",
      "Epoch 29/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7434 - acc: 0.0026 - val_loss: 4.8336 - val_acc: 7.9844e-04\n",
      "Epoch 30/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.6008 - acc: 0.0026 - val_loss: 4.7642 - val_acc: 7.9844e-04\n",
      "Epoch 31/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3522 - acc: 0.0026 - val_loss: 4.7759 - val_acc: 7.9844e-04\n",
      "Epoch 32/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.7503 - acc: 0.0026 - val_loss: 4.5526 - val_acc: 7.9844e-04\n",
      "Epoch 33/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.5049 - acc: 0.0026 - val_loss: 4.5729 - val_acc: 7.9844e-04\n",
      "Epoch 34/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.5913 - acc: 0.0026 - val_loss: 4.5135 - val_acc: 7.9844e-04\n",
      "Epoch 35/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.6227 - acc: 0.0026 - val_loss: 4.4158 - val_acc: 7.9844e-04\n",
      "Epoch 36/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.0114 - acc: 0.0026 - val_loss: 4.4152 - val_acc: 7.9844e-04\n",
      "Epoch 37/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.6394 - acc: 0.0026 - val_loss: 4.4264 - val_acc: 7.9844e-04\n",
      "Epoch 38/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7554 - acc: 0.0026 - val_loss: 4.4681 - val_acc: 7.9844e-04\n",
      "Epoch 39/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.9328 - acc: 0.0026 - val_loss: 4.4508 - val_acc: 7.9844e-04\n",
      "Epoch 40/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7485 - acc: 0.0026 - val_loss: 4.4416 - val_acc: 7.9844e-04\n",
      "Epoch 41/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.6807 - acc: 0.0026 - val_loss: 4.3046 - val_acc: 7.9844e-04\n",
      "Epoch 42/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.9783 - acc: 0.0026 - val_loss: 4.3051 - val_acc: 7.9844e-04\n",
      "Epoch 43/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.3474 - acc: 0.0026 - val_loss: 4.3987 - val_acc: 7.9844e-04\n",
      "Epoch 44/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.5599 - acc: 0.0026 - val_loss: 4.6474 - val_acc: 7.9844e-04\n",
      "Epoch 45/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.3126 - acc: 0.0026 - val_loss: 4.6643 - val_acc: 7.9844e-04\n",
      "Epoch 46/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.5212 - acc: 0.0026 - val_loss: 4.6459 - val_acc: 7.9844e-04\n",
      "Epoch 47/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.9856 - acc: 0.0026 - val_loss: 4.6378 - val_acc: 7.9844e-04\n",
      "Epoch 48/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.8402 - acc: 0.0026 - val_loss: 4.6421 - val_acc: 7.9844e-04\n",
      "Epoch 49/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.6939 - acc: 0.0026 - val_loss: 4.5928 - val_acc: 7.9844e-04\n",
      "Epoch 50/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.2858 - acc: 0.0026 - val_loss: 4.5273 - val_acc: 7.9844e-04\n",
      "22544/22544 [==============================] - 0s     \n",
      "\n",
      " Train Acc: 0.0017743080388754606, Test Acc: 0.0007984386174939573\n",
      " \n",
      " Current Layer Attributes - epochs:50 hidden layers:10 features count:4\n",
      "Train on 112720 samples, validate on 22544 samples\n",
      "Epoch 1/50\n",
      "112720/112720 [==============================] - 5s - loss: 3.3963 - acc: 3.5486e-05 - val_loss: 8.0952 - val_acc: 4.4358e-05\n",
      "Epoch 2/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.1981 - acc: 0.0052 - val_loss: 8.5042 - val_acc: 0.0018\n",
      "Epoch 3/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.8073 - acc: 0.0055 - val_loss: 8.6326 - val_acc: 0.0018\n",
      "Epoch 4/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.3942 - acc: 0.0055 - val_loss: 9.4381 - val_acc: 0.0018\n",
      "Epoch 5/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.0680 - acc: 0.0055 - val_loss: 9.2335 - val_acc: 0.0018\n",
      "Epoch 6/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.9608 - acc: 0.0055 - val_loss: 9.1144 - val_acc: 0.0018\n",
      "Epoch 7/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7053 - acc: 0.0055 - val_loss: 9.2347 - val_acc: 0.0018\n",
      "Epoch 8/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.0132 - acc: 0.0055 - val_loss: 9.1787 - val_acc: 0.0018\n",
      "Epoch 9/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.0742 - acc: 0.0055 - val_loss: 9.2158 - val_acc: 0.0018\n",
      "Epoch 10/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.3195 - acc: 0.0055 - val_loss: 9.3057 - val_acc: 0.0018\n",
      "Epoch 11/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.4683 - acc: 0.0055 - val_loss: 9.2002 - val_acc: 0.0018\n",
      "Epoch 12/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7002 - acc: 0.0055 - val_loss: 9.1445 - val_acc: 0.0018\n",
      "Epoch 13/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.4536 - acc: 0.0055 - val_loss: 9.0535 - val_acc: 0.0018\n",
      "Epoch 14/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.8853 - acc: 0.0055 - val_loss: 9.0120 - val_acc: 0.0018\n",
      "Epoch 15/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.2609 - acc: 0.0055 - val_loss: 9.1512 - val_acc: 0.0018\n",
      "Epoch 16/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.8422 - acc: 0.0055 - val_loss: 9.2431 - val_acc: 0.0018\n",
      "Epoch 17/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.0169 - acc: 0.0055 - val_loss: 9.2302 - val_acc: 0.0018\n",
      "Epoch 18/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.9506 - acc: 0.0055 - val_loss: 9.1376 - val_acc: 0.0018\n",
      "Epoch 19/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.8049 - acc: 0.0055 - val_loss: 9.0599 - val_acc: 0.0018\n",
      "Epoch 20/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.9375 - acc: 0.0055 - val_loss: 9.0443 - val_acc: 0.0018\n",
      "Epoch 21/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.6776 - acc: 0.0055 - val_loss: 9.0286 - val_acc: 0.0018\n",
      "Epoch 22/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.5634 - acc: 0.0055 - val_loss: 8.9431 - val_acc: 0.0018\n",
      "Epoch 23/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.4852 - acc: 0.0055 - val_loss: 8.9126 - val_acc: 0.0018\n",
      "Epoch 24/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.8830 - acc: 0.0055 - val_loss: 8.5604 - val_acc: 0.0018\n",
      "Epoch 25/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.8879 - acc: 0.0055 - val_loss: 8.4552 - val_acc: 0.0018\n",
      "Epoch 26/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.6047 - acc: 0.0055 - val_loss: 8.3789 - val_acc: 0.0018\n",
      "Epoch 27/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5749 - acc: 0.0055 - val_loss: 8.4544 - val_acc: 0.0018\n",
      "Epoch 28/50\n",
      "112720/112720 [==============================] - 2s - loss: 3.0646 - acc: 0.0055 - val_loss: 8.5321 - val_acc: 0.0018\n",
      "Epoch 29/50\n",
      "112720/112720 [==============================] - 2s - loss: 3.2381 - acc: 0.0055 - val_loss: 8.2757 - val_acc: 0.0018\n",
      "Epoch 30/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.5887 - acc: 0.0055 - val_loss: 8.0622 - val_acc: 0.0018\n",
      "Epoch 31/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.0974 - acc: 0.0055 - val_loss: 8.3020 - val_acc: 0.0018\n",
      "Epoch 32/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "112720/112720 [==============================] - 3s - loss: 2.4242 - acc: 0.0055 - val_loss: 8.4037 - val_acc: 0.0018\n",
      "Epoch 33/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.4745 - acc: 0.0055 - val_loss: 8.2708 - val_acc: 0.0018\n",
      "Epoch 34/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7765 - acc: 0.0055 - val_loss: 8.3727 - val_acc: 0.0018\n",
      "Epoch 35/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.9161 - acc: 0.0055 - val_loss: 8.4060 - val_acc: 0.0018\n",
      "Epoch 36/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.9246 - acc: 0.0055 - val_loss: 8.1542 - val_acc: 0.0018\n",
      "Epoch 37/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7420 - acc: 0.0055 - val_loss: 8.4035 - val_acc: 0.0018\n",
      "Epoch 38/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.4429 - acc: 0.0055 - val_loss: 8.4214 - val_acc: 0.0018\n",
      "Epoch 39/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.6584 - acc: 0.0055 - val_loss: 8.4434 - val_acc: 0.0018\n",
      "Epoch 40/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.0393 - acc: 0.0055 - val_loss: 7.6838 - val_acc: 0.0018\n",
      "Epoch 41/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.8702 - acc: 0.0055 - val_loss: 7.5828 - val_acc: 0.0018\n",
      "Epoch 42/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7995 - acc: 0.0055 - val_loss: 7.5339 - val_acc: 0.0018\n",
      "Epoch 43/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.8634 - acc: 0.0055 - val_loss: 7.6447 - val_acc: 0.0018\n",
      "Epoch 44/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.5510 - acc: 0.0055 - val_loss: 7.6867 - val_acc: 0.0018\n",
      "Epoch 45/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.8681 - acc: 0.0055 - val_loss: 7.6439 - val_acc: 0.0018\n",
      "Epoch 46/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.6799 - acc: 0.0055 - val_loss: 7.7204 - val_acc: 0.0018\n",
      "Epoch 47/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7177 - acc: 0.0055 - val_loss: 8.2255 - val_acc: 0.0018\n",
      "Epoch 48/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.9075 - acc: 0.0055 - val_loss: 7.5577 - val_acc: 0.0018\n",
      "Epoch 49/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.2139 - acc: 0.0055 - val_loss: 7.5475 - val_acc: 0.0018\n",
      "Epoch 50/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.8122 - acc: 0.0055 - val_loss: 7.5741 - val_acc: 0.0018\n",
      "22544/22544 [==============================] - 0s     \n",
      "\n",
      " Train Acc: 0.004790631704963744, Test Acc: 0.0017743080388754606\n",
      " \n",
      " Current Layer Attributes - epochs:50 hidden layers:10 features count:8\n",
      "Train on 112720 samples, validate on 22544 samples\n",
      "Epoch 1/50\n",
      "112720/112720 [==============================] - 5s - loss: 2.5671 - acc: 0.0028 - val_loss: 13.1393 - val_acc: 0.0136\n",
      "Epoch 2/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.3778 - acc: 0.0036 - val_loss: 11.0594 - val_acc: 0.0073\n",
      "Epoch 3/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.2954 - acc: 5.3229e-05 - val_loss: 11.0418 - val_acc: 0.0000e+00\n",
      "Epoch 4/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.2465 - acc: 2.6615e-04 - val_loss: 10.9809 - val_acc: 0.0042\n",
      "Epoch 5/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.3437 - acc: 4.2583e-04 - val_loss: 10.9481 - val_acc: 0.0042\n",
      "Epoch 6/50\n",
      "112720/112720 [==============================] - 3s - loss: 1.8396 - acc: 4.2583e-04 - val_loss: 10.7832 - val_acc: 0.0042\n",
      "Epoch 7/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.5754 - acc: 4.2583e-04 - val_loss: 10.9090 - val_acc: 0.0042\n",
      "Epoch 8/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.6005 - acc: 4.7906e-04 - val_loss: 9.0526 - val_acc: 0.0042\n",
      "Epoch 9/50\n",
      "112720/112720 [==============================] - 3s - loss: 1.9696 - acc: 4.7019e-04 - val_loss: 8.9888 - val_acc: 0.0042\n",
      "Epoch 10/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.4371 - acc: 4.7019e-04 - val_loss: 8.8715 - val_acc: 0.0042\n",
      "Epoch 11/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.2289 - acc: 4.7019e-04 - val_loss: 8.9705 - val_acc: 0.0042\n",
      "Epoch 12/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.2622 - acc: 4.2583e-04 - val_loss: 8.9375 - val_acc: 0.0042\n",
      "Epoch 13/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.2106 - acc: 4.2583e-04 - val_loss: 8.9514 - val_acc: 0.0042\n",
      "Epoch 14/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.2665 - acc: 4.2583e-04 - val_loss: 8.9628 - val_acc: 0.0042\n",
      "Epoch 15/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.0420 - acc: 4.2583e-04 - val_loss: 9.0210 - val_acc: 0.0042\n",
      "Epoch 16/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.3112 - acc: 4.2583e-04 - val_loss: 9.0225 - val_acc: 0.0042\n",
      "Epoch 17/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.3788 - acc: 4.2583e-04 - val_loss: 9.2275 - val_acc: 0.0042\n",
      "Epoch 18/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.1769 - acc: 4.2583e-04 - val_loss: 9.0615 - val_acc: 0.0042\n",
      "Epoch 19/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.4004 - acc: 4.2583e-04 - val_loss: 9.0925 - val_acc: 0.0042\n",
      "Epoch 20/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.1839 - acc: 4.2583e-04 - val_loss: 9.0900 - val_acc: 0.0042\n",
      "Epoch 21/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.0002 - acc: 4.2583e-04 - val_loss: 9.0583 - val_acc: 0.0042\n",
      "Epoch 22/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.4271 - acc: 4.2583e-04 - val_loss: 9.0375 - val_acc: 0.0042\n",
      "Epoch 23/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3038 - acc: 4.2583e-04 - val_loss: 9.1301 - val_acc: 0.0042\n",
      "Epoch 24/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.1590 - acc: 4.2583e-04 - val_loss: 9.1265 - val_acc: 0.0042\n",
      "Epoch 25/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4349 - acc: 4.2583e-04 - val_loss: 9.4254 - val_acc: 0.0042\n",
      "Epoch 26/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.4079 - acc: 4.2583e-04 - val_loss: 9.5176 - val_acc: 0.0042\n",
      "Epoch 27/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.6136 - acc: 4.2583e-04 - val_loss: 9.5061 - val_acc: 0.0042\n",
      "Epoch 28/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.8967 - acc: 4.2583e-04 - val_loss: 9.5101 - val_acc: 0.0042\n",
      "Epoch 29/50\n",
      "112720/112720 [==============================] - 3s - loss: 1.9754 - acc: 4.2583e-04 - val_loss: 9.5150 - val_acc: 0.0042\n",
      "Epoch 30/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.4067 - acc: 4.2583e-04 - val_loss: 9.4506 - val_acc: 0.0042\n",
      "Epoch 31/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.2658 - acc: 4.2583e-04 - val_loss: 9.3505 - val_acc: 0.0042\n",
      "Epoch 32/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.2220 - acc: 4.2583e-04 - val_loss: 9.3156 - val_acc: 0.0042\n",
      "Epoch 33/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.5037 - acc: 4.2583e-04 - val_loss: 9.2125 - val_acc: 0.0042\n",
      "Epoch 34/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.2796 - acc: 4.2583e-04 - val_loss: 9.1905 - val_acc: 0.0042\n",
      "Epoch 35/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.3898 - acc: 4.2583e-04 - val_loss: 9.1498 - val_acc: 0.0042\n",
      "Epoch 36/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.0634 - acc: 4.2583e-04 - val_loss: 9.1738 - val_acc: 0.0042\n",
      "Epoch 37/50\n",
      "112720/112720 [==============================] - 3s - loss: 1.8915 - acc: 4.2583e-04 - val_loss: 9.1337 - val_acc: 0.0042\n",
      "Epoch 38/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.3224 - acc: 4.2583e-04 - val_loss: 9.1197 - val_acc: 0.0042\n",
      "Epoch 39/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.2941 - acc: 4.2583e-04 - val_loss: 9.1307 - val_acc: 0.0042\n",
      "Epoch 40/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.1425 - acc: 4.2583e-04 - val_loss: 9.1738 - val_acc: 0.0042\n",
      "Epoch 41/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.6647 - acc: 4.2583e-04 - val_loss: 9.2398 - val_acc: 0.0042\n",
      "Epoch 42/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "112720/112720 [==============================] - 3s - loss: 2.1348 - acc: 4.2583e-04 - val_loss: 9.2473 - val_acc: 0.0042\n",
      "Epoch 43/50\n",
      "112720/112720 [==============================] - 3s - loss: 1.9647 - acc: 4.2583e-04 - val_loss: 9.1289 - val_acc: 0.0042\n",
      "Epoch 44/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.2190 - acc: 4.2583e-04 - val_loss: 9.1283 - val_acc: 0.0042\n",
      "Epoch 45/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.3317 - acc: 4.2583e-04 - val_loss: 9.1243 - val_acc: 0.0042\n",
      "Epoch 46/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.2283 - acc: 4.2583e-04 - val_loss: 9.1263 - val_acc: 0.0042\n",
      "Epoch 47/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.4206 - acc: 4.2583e-04 - val_loss: 9.0753 - val_acc: 0.0042\n",
      "Epoch 48/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.5235 - acc: 4.2583e-04 - val_loss: 9.1002 - val_acc: 0.0042\n",
      "Epoch 49/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.5796 - acc: 4.2583e-04 - val_loss: 9.1942 - val_acc: 0.0042\n",
      "Epoch 50/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.5782 - acc: 4.2583e-04 - val_loss: 9.1788 - val_acc: 0.0042\n",
      "22544/22544 [==============================] - 0s     \n",
      "\n",
      " Train Acc: 0.00044357700971886516, Test Acc: 0.004213981592329219\n",
      " \n",
      " Current Layer Attributes - epochs:50 hidden layers:10 features count:16\n",
      "Train on 112720 samples, validate on 22544 samples\n",
      "Epoch 1/50\n",
      "112720/112720 [==============================] - 5s - loss: 3.0152 - acc: 6.5649e-04 - val_loss: 15.7038 - val_acc: 5.7665e-04\n",
      "Epoch 2/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7528 - acc: 6.2988e-04 - val_loss: 16.0054 - val_acc: 5.7665e-04\n",
      "Epoch 3/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.8331 - acc: 6.2988e-04 - val_loss: 15.8033 - val_acc: 5.7665e-04\n",
      "Epoch 4/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7083 - acc: 6.2988e-04 - val_loss: 15.7618 - val_acc: 5.7665e-04\n",
      "Epoch 5/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.6037 - acc: 6.2988e-04 - val_loss: 15.8684 - val_acc: 5.7665e-04\n",
      "Epoch 6/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.3256 - acc: 6.2988e-04 - val_loss: 15.8199 - val_acc: 5.7665e-04\n",
      "Epoch 7/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.5636 - acc: 6.2988e-04 - val_loss: 15.9021 - val_acc: 5.7665e-04\n",
      "Epoch 8/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.3698 - acc: 6.2988e-04 - val_loss: 16.0123 - val_acc: 5.7665e-04\n",
      "Epoch 9/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.9344 - acc: 6.2988e-04 - val_loss: 16.0267 - val_acc: 5.7665e-04\n",
      "Epoch 10/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7135 - acc: 6.2988e-04 - val_loss: 16.0407 - val_acc: 5.7665e-04\n",
      "Epoch 11/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.5966 - acc: 6.2988e-04 - val_loss: 16.0865 - val_acc: 5.7665e-04\n",
      "Epoch 12/50\n",
      "112720/112720 [==============================] - 3s - loss: 3.0348 - acc: 6.2988e-04 - val_loss: 16.0838 - val_acc: 5.7665e-04\n",
      "Epoch 13/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.9569 - acc: 6.2988e-04 - val_loss: 16.0809 - val_acc: 5.7665e-04\n",
      "Epoch 14/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.4934 - acc: 6.2988e-04 - val_loss: 17.9665 - val_acc: 5.7665e-04\n",
      "Epoch 15/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.8479 - acc: 6.2988e-04 - val_loss: 17.9820 - val_acc: 5.7665e-04\n",
      "Epoch 16/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7340 - acc: 6.2988e-04 - val_loss: 17.9990 - val_acc: 5.7665e-04\n",
      "Epoch 17/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7466 - acc: 6.2988e-04 - val_loss: 17.9541 - val_acc: 5.7665e-04\n",
      "Epoch 18/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.4032 - acc: 6.2988e-04 - val_loss: 17.9940 - val_acc: 5.7665e-04\n",
      "Epoch 19/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.5894 - acc: 6.2988e-04 - val_loss: 17.9692 - val_acc: 5.7665e-04\n",
      "Epoch 20/50\n",
      "112720/112720 [==============================] - 2s - loss: 3.0272 - acc: 6.2988e-04 - val_loss: 18.0093 - val_acc: 5.7665e-04\n",
      "Epoch 21/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5956 - acc: 6.2988e-04 - val_loss: 18.0590 - val_acc: 5.7665e-04\n",
      "Epoch 22/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.8124 - acc: 6.2988e-04 - val_loss: 18.1139 - val_acc: 5.7665e-04\n",
      "Epoch 23/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5715 - acc: 6.2988e-04 - val_loss: 18.0807 - val_acc: 5.7665e-04\n",
      "Epoch 24/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5800 - acc: 6.2988e-04 - val_loss: 18.1139 - val_acc: 5.7665e-04\n",
      "Epoch 25/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4996 - acc: 6.2988e-04 - val_loss: 18.0921 - val_acc: 5.7665e-04\n",
      "Epoch 26/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3527 - acc: 6.2988e-04 - val_loss: 18.0846 - val_acc: 5.7665e-04\n",
      "Epoch 27/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.7729 - acc: 6.2988e-04 - val_loss: 18.1220 - val_acc: 5.7665e-04\n",
      "Epoch 28/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.6025 - acc: 6.2988e-04 - val_loss: 18.2349 - val_acc: 5.7665e-04\n",
      "Epoch 29/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.6002 - acc: 6.2988e-04 - val_loss: 18.1951 - val_acc: 5.7665e-04\n",
      "Epoch 30/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2085 - acc: 6.2988e-04 - val_loss: 18.2025 - val_acc: 5.7665e-04\n",
      "Epoch 31/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.6136 - acc: 6.2988e-04 - val_loss: 18.1924 - val_acc: 5.7665e-04\n",
      "Epoch 32/50\n",
      "112720/112720 [==============================] - 2s - loss: 3.0684 - acc: 6.2988e-04 - val_loss: 18.2733 - val_acc: 5.7665e-04\n",
      "Epoch 33/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2810 - acc: 6.2988e-04 - val_loss: 18.1265 - val_acc: 5.7665e-04\n",
      "Epoch 34/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5151 - acc: 6.2988e-04 - val_loss: 18.1427 - val_acc: 5.7665e-04\n",
      "Epoch 35/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4967 - acc: 6.2988e-04 - val_loss: 18.0935 - val_acc: 5.7665e-04\n",
      "Epoch 36/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4051 - acc: 6.2988e-04 - val_loss: 18.0928 - val_acc: 5.7665e-04\n",
      "Epoch 37/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.6460 - acc: 6.2988e-04 - val_loss: 18.0962 - val_acc: 5.7665e-04\n",
      "Epoch 38/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.6048 - acc: 6.2988e-04 - val_loss: 18.1042 - val_acc: 5.7665e-04\n",
      "Epoch 39/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.8485 - acc: 6.2988e-04 - val_loss: 18.0410 - val_acc: 5.7665e-04\n",
      "Epoch 40/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.7882 - acc: 6.2988e-04 - val_loss: 18.0404 - val_acc: 5.7665e-04\n",
      "Epoch 41/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5377 - acc: 6.2988e-04 - val_loss: 18.0504 - val_acc: 5.7665e-04\n",
      "Epoch 42/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.8169 - acc: 6.2988e-04 - val_loss: 17.9973 - val_acc: 5.7665e-04\n",
      "Epoch 43/50\n",
      "112720/112720 [==============================] - 2s - loss: 3.0533 - acc: 6.2988e-04 - val_loss: 18.0320 - val_acc: 5.7665e-04\n",
      "Epoch 44/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.6503 - acc: 6.2988e-04 - val_loss: 18.0231 - val_acc: 5.7665e-04\n",
      "Epoch 45/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5907 - acc: 6.2988e-04 - val_loss: 18.0200 - val_acc: 5.7665e-04\n",
      "Epoch 46/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.6941 - acc: 6.2988e-04 - val_loss: 20.0984 - val_acc: 5.7665e-04\n",
      "Epoch 47/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4637 - acc: 6.2988e-04 - val_loss: 20.1077 - val_acc: 5.7665e-04\n",
      "Epoch 48/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2673 - acc: 6.2988e-04 - val_loss: 20.1719 - val_acc: 5.7665e-04\n",
      "Epoch 49/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "112720/112720 [==============================] - 2s - loss: 2.5510 - acc: 6.2988e-04 - val_loss: 20.1741 - val_acc: 5.7665e-04\n",
      "Epoch 50/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4084 - acc: 6.2988e-04 - val_loss: 20.1980 - val_acc: 5.7665e-04\n",
      "19726/22544 [=========================>....] - ETA: 0s\n",
      " Train Acc: 0.00044357700971886516, Test Acc: 0.0005766501126345247\n",
      " \n",
      " Current Layer Attributes - epochs:50 hidden layers:10 features count:32\n",
      "Train on 112720 samples, validate on 22544 samples\n",
      "Epoch 1/50\n",
      "112720/112720 [==============================] - 4s - loss: 2.7738 - acc: 0.0037 - val_loss: 28.9895 - val_acc: 0.0063\n",
      "Epoch 2/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.7571 - acc: 0.0044 - val_loss: 29.4855 - val_acc: 0.0063\n",
      "Epoch 3/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.7743 - acc: 0.0044 - val_loss: 29.5967 - val_acc: 0.0062\n",
      "Epoch 4/50\n",
      "112720/112720 [==============================] - 2s - loss: 3.1826 - acc: 0.0044 - val_loss: 29.7883 - val_acc: 0.0063\n",
      "Epoch 5/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3408 - acc: 0.0044 - val_loss: 29.6997 - val_acc: 0.0059\n",
      "Epoch 6/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.7549 - acc: 0.0042 - val_loss: 29.6625 - val_acc: 0.0058\n",
      "Epoch 7/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.6605 - acc: 0.0043 - val_loss: 29.6337 - val_acc: 0.0059\n",
      "Epoch 8/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.9443 - acc: 0.0044 - val_loss: 30.8648 - val_acc: 0.0063\n",
      "Epoch 9/50\n",
      "112720/112720 [==============================] - 2s - loss: 3.1357 - acc: 0.0045 - val_loss: 31.8837 - val_acc: 0.0063\n",
      "Epoch 10/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.7916 - acc: 0.0045 - val_loss: 31.9559 - val_acc: 0.0063\n",
      "Epoch 11/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.8399 - acc: 0.0045 - val_loss: 31.9401 - val_acc: 0.0063\n",
      "Epoch 12/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5722 - acc: 0.0045 - val_loss: 31.9556 - val_acc: 0.0063\n",
      "Epoch 13/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.8665 - acc: 0.0045 - val_loss: 31.9436 - val_acc: 0.0063\n",
      "Epoch 14/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.7423 - acc: 0.0045 - val_loss: 32.0014 - val_acc: 0.0063\n",
      "Epoch 15/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.9396 - acc: 0.0045 - val_loss: 32.0549 - val_acc: 0.0063\n",
      "Epoch 16/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.9737 - acc: 0.0045 - val_loss: 32.0651 - val_acc: 0.0063\n",
      "Epoch 17/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.9235 - acc: 0.0045 - val_loss: 32.0857 - val_acc: 0.0063\n",
      "Epoch 18/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5101 - acc: 0.0045 - val_loss: 32.0762 - val_acc: 0.0063\n",
      "Epoch 19/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5167 - acc: 0.0045 - val_loss: 32.0711 - val_acc: 0.0063\n",
      "Epoch 20/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.8838 - acc: 0.0045 - val_loss: 32.0877 - val_acc: 0.0063\n",
      "Epoch 21/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5488 - acc: 0.0045 - val_loss: 34.0120 - val_acc: 0.0063\n",
      "Epoch 22/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.7345 - acc: 0.0045 - val_loss: 34.0150 - val_acc: 0.0063\n",
      "Epoch 23/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.6311 - acc: 0.0045 - val_loss: 33.9972 - val_acc: 0.0063\n",
      "Epoch 24/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.3727 - acc: 0.0045 - val_loss: 33.9922 - val_acc: 0.0063\n",
      "Epoch 25/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.6066 - acc: 0.0045 - val_loss: 34.0094 - val_acc: 0.0063\n",
      "Epoch 26/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4262 - acc: 0.0045 - val_loss: 34.0026 - val_acc: 0.0063\n",
      "Epoch 27/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.2488 - acc: 0.0045 - val_loss: 33.9433 - val_acc: 0.0063\n",
      "Epoch 28/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.4355 - acc: 0.0045 - val_loss: 33.9592 - val_acc: 0.0063\n",
      "Epoch 29/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.4708 - acc: 0.0045 - val_loss: 33.9517 - val_acc: 0.0063\n",
      "Epoch 30/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.8763 - acc: 0.0045 - val_loss: 33.9300 - val_acc: 0.0063\n",
      "Epoch 31/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.5323 - acc: 0.0045 - val_loss: 33.9315 - val_acc: 0.0063\n",
      "Epoch 32/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.8727 - acc: 0.0045 - val_loss: 33.9980 - val_acc: 0.0063\n",
      "Epoch 33/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.6311 - acc: 0.0045 - val_loss: 33.9998 - val_acc: 0.0063\n",
      "Epoch 34/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.8769 - acc: 0.0045 - val_loss: 34.0049 - val_acc: 0.0063\n",
      "Epoch 35/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.5519 - acc: 0.0045 - val_loss: 34.0085 - val_acc: 0.0063\n",
      "Epoch 36/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7630 - acc: 0.0045 - val_loss: 34.0177 - val_acc: 0.0063\n",
      "Epoch 37/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.3333 - acc: 0.0045 - val_loss: 34.0144 - val_acc: 0.0063\n",
      "Epoch 38/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.5894 - acc: 0.0045 - val_loss: 34.0182 - val_acc: 0.0063\n",
      "Epoch 39/50\n",
      "112720/112720 [==============================] - 2s - loss: 3.1464 - acc: 0.0045 - val_loss: 33.9726 - val_acc: 0.0063\n",
      "Epoch 40/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.3444 - acc: 0.0045 - val_loss: 34.1293 - val_acc: 0.0063\n",
      "Epoch 41/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.4264 - acc: 0.0045 - val_loss: 33.9657 - val_acc: 0.0063\n",
      "Epoch 42/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7262 - acc: 0.0045 - val_loss: 33.8195 - val_acc: 0.0063\n",
      "Epoch 43/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.6921 - acc: 0.0045 - val_loss: 33.8086 - val_acc: 0.0063\n",
      "Epoch 44/50\n",
      "112720/112720 [==============================] - 2s - loss: 3.0846 - acc: 0.0045 - val_loss: 33.8085 - val_acc: 0.0063\n",
      "Epoch 45/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.3014 - acc: 0.0045 - val_loss: 33.8640 - val_acc: 0.0063\n",
      "Epoch 46/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.6692 - acc: 0.0045 - val_loss: 33.8606 - val_acc: 0.0063\n",
      "Epoch 47/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.3880 - acc: 0.0045 - val_loss: 33.8651 - val_acc: 0.0063\n",
      "Epoch 48/50\n",
      "112720/112720 [==============================] - 3s - loss: 2.7186 - acc: 0.0045 - val_loss: 33.8580 - val_acc: 0.0063\n",
      "Epoch 49/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.9074 - acc: 0.0045 - val_loss: 33.8665 - val_acc: 0.0063\n",
      "Epoch 50/50\n",
      "112720/112720 [==============================] - 2s - loss: 2.9857 - acc: 0.0045 - val_loss: 33.9075 - val_acc: 0.0063\n",
      "21135/22544 [===========================>..] - ETA: 0s\n",
      " Train Acc: 0.003992193087469786, Test Acc: 0.006254435749724507\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "#features_arr = [4, 16, 32, 256, 1024]\n",
    "#hidden_layers_arr = [2, 6, 10, 100]\n",
    "\n",
    "features_arr = [2, 4,  8, 16, 32]\n",
    "hidden_layers_arr = [2, 6, 10]\n",
    "\n",
    "epoch_arr = [50]\n",
    "\n",
    "score = namedtuple(\"score\", ['epoch', 'no_of_features','hidden_layers','train_score', 'test_score'])\n",
    "scores = []\n",
    "predictions = pd.DataFrame()\n",
    "\n",
    "for e, h, f in itertools.product(epoch_arr, hidden_layers_arr, features_arr):\n",
    "    \n",
    "    print(\" \\n Current Layer Attributes - epochs:{} hidden layers:{} features count:{}\".format(e,h,f))\n",
    "    latent_dim = f\n",
    "    epochs = e\n",
    "    hidden_layers = h\n",
    "\n",
    "    Train.train()\n",
    "\n",
    "    optimizer = keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-04, decay=0.1)\n",
    "    vae_model = Model(inputs = Train.x, outputs = Train.x_ )\n",
    "    vae_model.compile(optimizer = optimizer, loss = vae_loss, metrics = ['accuracy'] )\n",
    "\n",
    "    train_size = x_train.shape[0] - x_train.shape[0]%batch_size\n",
    "    valid_size = x_valid.shape[0] - x_valid.shape[0]%batch_size\n",
    "\n",
    "    vae_model.fit(x = x_train[:train_size,:], y = x_train[:train_size,:], \n",
    "                  shuffle=True, epochs=epochs, \n",
    "                  batch_size = batch_size, \n",
    "                  validation_data = (x_test, x_test),\n",
    "                  verbose = 1)\n",
    "    \n",
    "    score_train = vae_model.evaluate(x_valid[:valid_size,:], y = x_valid[:valid_size,:],\n",
    "                               batch_size = batch_size,\n",
    "                               verbose = 1)\n",
    "    \n",
    "    score_test = vae_model.evaluate(x_test, y = x_test,\n",
    "                           batch_size = batch_size,\n",
    "                           verbose = 1)\n",
    "    \n",
    "    scores.append(score(e,f,h,score_train[-1], score_test[-1])) #score_test[-1]))\n",
    "    \n",
    "    print(\"\\n Train Acc: {}, Test Acc: {}\".format(score_train[-1], \n",
    "                                                  score_test[-1])  )\n",
    "    \n",
    "scores = pd.DataFrame(scores)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-10T18:04:58.796959Z",
     "start_time": "2017-05-10T18:04:58.782465Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epoch</th>\n",
       "      <th>no_of_features</th>\n",
       "      <th>hidden_layers</th>\n",
       "      <th>train_score</th>\n",
       "      <th>test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>0.034422</td>\n",
       "      <td>0.039257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>0.003903</td>\n",
       "      <td>0.010424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.005500</td>\n",
       "      <td>0.008694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.001331</td>\n",
       "      <td>0.008206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>10</td>\n",
       "      <td>0.003992</td>\n",
       "      <td>0.006254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000444</td>\n",
       "      <td>0.004214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0.003992</td>\n",
       "      <td>0.003327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>0.004791</td>\n",
       "      <td>0.001774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>0.005057</td>\n",
       "      <td>0.001597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>50</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>0.004081</td>\n",
       "      <td>0.001198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0.004436</td>\n",
       "      <td>0.001020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.001774</td>\n",
       "      <td>0.000798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>50</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000444</td>\n",
       "      <td>0.000577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000798</td>\n",
       "      <td>0.000089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    epoch  no_of_features  hidden_layers  train_score  test_score\n",
       "3      50              16              2     0.034422    0.039257\n",
       "9      50              32              6     0.003903    0.010424\n",
       "0      50               2              2     0.005500    0.008694\n",
       "1      50               4              2     0.001331    0.008206\n",
       "14     50              32             10     0.003992    0.006254\n",
       "12     50               8             10     0.000444    0.004214\n",
       "2      50               8              2     0.003992    0.003327\n",
       "11     50               4             10     0.004791    0.001774\n",
       "4      50              32              2     0.005057    0.001597\n",
       "8      50              16              6     0.004081    0.001198\n",
       "5      50               2              6     0.004436    0.001020\n",
       "10     50               2             10     0.001774    0.000798\n",
       "13     50              16             10     0.000444    0.000577\n",
       "7      50               8              6     0.000798    0.000089\n",
       "6      50               4              6     0.000000    0.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.sort_values(\"test_score\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-05-10T18:04:58.823287Z",
     "start_time": "2017-05-10T18:04:58.798571Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scores.to_pickle(\"dataset/vae_only_feature_extraction_scores.pkl\")"
   ]
  }
 ],
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